Thèses sur le sujet « MAINTENANCE PREDICTION »
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1
Morrison, David J. « Prediction of software maintenance costs ». Thesis, Edinburgh Napier University, 2001. http://researchrepository.napier.ac.uk/Output/3601.
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This thesis is concerned with predicting the costs of maintaining a computer program prior to the software being developed. The ubiquitous nature of software means that software maintenance is an important activity, and evidence exists to support the contention that it is the largest and most costly area of endeavour within the software domain. Given the levels of expenditure associated with software maintenance, an ability to quantify future costs and address the determinants of these costs can assist in the planning and allocation of resources. Despite the importance of this field only a limited understanding of the factors that determine future maintenance costs exists, and maintenance estimation is more frequently applied to existing software. A hypothesis has been postulated that suggests the inherent maintainability of the software, the scale of the activity and the degree of change that pertains will determine future software maintenance costs. The variables that contribute to the maintainability of the software have been explored through a survey of past projects, which was undertaken using a questionnaire. This was designed with assistance from three separate teams of professional software engineers. The questionnaire requires 69 numerical or ordinal responses to a series of questions pertaining to characteristics including program structure, computer architecture, software development methodology, project management processes and maintenance outcomes. Factor analysis methods were applied and five of the most powerful predictors are identified. A linear model capable of predicting maintainability has been developed. Validation was undertaken through a series of follow-up interviews with several survey respondents, and by further statistical analysis utilising hold-out samples and structural equation modelling. The model was subsequently used to develop predictive tools intended to provide management support by both providing a categorical assessment of future maintainability, and a quantitative estimate of probable maintenance costs. The distinction between essential corrective maintenance, and other elective forms of maintenance is considered. Conclusions are drawn regarding the efficacy and limitations of tools that can be developedt o supportm anagemendt ecisionm aking. Subjectt o further work with a largers ampleo f projects,p referablyf rom within a singleo rganisationi,t is concluded i that useful tools could be developed to make both categorical ('acceptable' versus 'not acceptable') and static (initial) quantitative predictions. The latter is dependent on the availability of a software development estimate. Some useful predictive methods have also been applied to dynamic (continuing) quantitative prediction in circumstances where a trend develops in successive forecasts. Recommendationfosr furtherw ork arep rovided.T hesei nclude: U Factor analysis and linear regression has been applied to a sample of past software projects from a variety of application areas to identify important input variables for use in a maintainability prediction model. Maintainability is regarded as an important determinant of maintenance resource requirements. The performance of these variables within a single organisation should be confirmed by undertaking a further factor analysis and linear regression on projects from within the target organisation. u The robustness of model design within this target organisation should be considered by applying a sensitivity analysis to the input variables. u This single organisation maintainability predictor model design should be validated by confirmatory interviews with specialists and users from within the target organisation. u Aggregate scale has been identified as another predictor of overall maintenance resource requirements, and the relationship between development and maintenance effort explored for the general case. It is desirable that development and corrective maintenance scale relationships should be explored within a single organisation. Within this environment the association between standardised effort and maintainability should be confirmed, and the value of the logistic model as a descriptor of the relationship verified. u The approacht o quantifying non-correctivem aintenanceth at has been outlined requiresf iirther developmentT. he relationshipb etweena nnualc hanget raffic and maintenancec ostss houldb e modelled,a ssuminga prior knowledgeo f the scale and maintainability determinants. uA sensitivity analysis should be applied to the predictive system that has been developed, recognising the potential for error in the values of the input variables that may pertain. uA goal of this further research should be the development of a suite of soft tools, designed to enable the user to develop a software maintenance estimation system.
2
Ishihara, Yasuo. « Prediction of human error in rail car maintenance ». Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10629.
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Hartmann, Jens. « Analysis of maintenance records to support prediction of maintenance requirements in the German Army ». Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA392054.
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Kumbala, Bharadwaj Reddy. « Predictive Maintenance of NOx Sensor using Deep Learning : Time series prediction with encoder-decoder LSTM ». Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18668.
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In automotive industry there is a growing need for predicting the failure of a component, to achieve the cost saving and customer satisfaction. As failure in a component leads to the work breakdown for the customer. This paper describes an effort in making a prediction failure monitoring model for NOx sensor in trucks. It is a component that used to measure the level of nitrogen oxide emission from the truck. The NOx sensor has chosen because its failure leads to the slowdown of engine efficiency and it is fragile and costly to replace. The data from a good and contaminated NOx sensor which is collated from the test rigs is used the input to the model. This work in this paper shows approach of complementing the Deep Learning models with Machine Learning algorithm to achieve the results. In this work LSTMs are used to detect the gain in NOx sensor and Encoder-Decoder LSTM is used to predict the variables. On top of it Multiple Linear Regression model is used to achieve the end results. The performance of the monitoring model is promising. The approach described in this paper is a general model and not specific to this component, but also can be used for other sensors too as it has a universal kind of approach.
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Podda, G. « PREDICTION OF OPTIMAL WARFARIN MAINTENANCE DOSE USING ADVANCED ARTIFICIAL NEURAL NETWORKS ». Doctoral thesis, Università degli Studi di Milano, 2013. http://hdl.handle.net/2434/219087.
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Introduction. The individual response to vitamin K antagonists (VKA) is highly variable, being influenced by clinical factors and genetic variants of enzymes that are involved in the metabolism of VKA (CYP2C)) and vitamin K (VKORC1). Currently, the dose of VKA is adjusted based on measurements of the prothrombin time. In the last years, mathematical algorithms were developed for estimating the appropriate VKA dose, based on different mathematical approaches working on clinical and genetic data. Artificial Neural Networks (ANN) are computerized algorithms resembling interactive processes of the human brain, which allow to study very complex non-linear phenomena like biological systems. Aim. To evaluate the performance of new generation ANN on a large data base of patients on chronic VKA treatment. Methods. Clinical and genetic data from 377 patients (186 m; 191 f) treated with a VKA (warfarin) average weekly maintenance dose (WMD) of 23.7 mg (11.5 SD) were used to create a dose algorithm. Forty-eight variables, including demographic, clinical and genetic data (5 CYP2C9 and 3 VKORC1 genetic variants) were entered into Twist® system, which can select fundamental variables during their evolution in search for the best predictive model. The final model, based on 23 variables expressed a functional approximation of the actual dose within a validation protocol based on a tripartite division of the data set (training, testing, validation). Results. In the validation cohort, the pharmacogenetic algorithm reached high accuracy, with an average absolute error of 5.7 mg WMD. In the subset of patients requiring ≤21 mg (45 % of the cohort) and 21-49 mg (51 % of the cohort) the absolute error was 3.86 mg and 5.45 with a high percentage of subjects being correctly identified (72%, 74% respectively). Conclusion. ANN can be applied successfully for VKA maintenance dose prediction and represent a robust basis for a prospective multicentre clinical trial of the efficacy of genetically informed dose estimation for patients who require VKA.
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Tse, Peter W. « Neural networks for machine fault diagnosis and life span prediction ». Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390518.
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Wan, Husain Wan Mohd Sufian Bin. « Maintainability prediction for aircraft mechanical components utilising aircraft feedback information ». Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/7272.
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The aim of this research is to propose an alternative approach to determine the
maintainability prediction for aircraft components. In this research, the author looks at
certain areas of the maintainability prediction process where missteps or
misapplications most commonly occur. The first of these is during the early stage of
the Design for Maintainability (DfMt) process. The author discovered the importance
of utilising historical information or feedback information. The second area is during
the maintainability prediction where the maintenance of components is quantified;
here, the author proposes having the maximum target for each individual
maintainability component.
This research attempts to utilise aircraft maintenance historical data and
information (i.e. feedback information systems). Aircraft feedback information
contains various types of information that could be used for future improvement
rather than just the failure elements. Literature shows that feedback information such
as Service Difficulty Reporting System (SDRS) and Air Accidents Investigation Branch,
(AAIB) reports have helped to identify the critical and sensitive components that need
more attention for further improvement.
This research consists of two elements. The first is to identity and analyse
historical data. The second is to identify existing maintainability prediction
methodologies and propose an improved methodology. The 10 years’ data from
Federal Aviation Administration (FAA) SDRS data of all aircraft were collected and
analysed in accordance with the proposed methodology before the processes of
maintainability allocation and prediction were carried out.
The maintainability was predicted to identify the potential task time for each
individual aircraft component. The predicted tasks time in this research has to be in
accordance with industrial real tasks time were possible. One of the identified
solutions is by using maintainability allocation methodology. The existing
maintainability allocation methodology was improved, tested, and validated by using
several case studies. The outcomes were found to be very successful.
Overall, this research has proposed a new methodology for maintainability
prediction by integrating two important elements: historical data information, and
maintainability allocation. The study shows that the aircraft maintenance related
feedback information systems analyses were very useful for deciding maintainabilityeffectiveness; these include planning, organising maintenance and design
improvement. There is no doubt that historical data information has the ability to
contribute an important role in design activities. The results also show that
maintainability is an importance measure that can be used as a guideline for managing
efforts made for the improvement of aircraft components.
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Kaidis, Christos. « Wind Turbine Reliability Prediction : A Scada Data Processing & ; Reliability Estimation Tool ». Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-221135.
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This research project discusses the life-cycle analysis of wind turbines through the processing of operational data from two modern European wind farms. A methodology for SCADA data processing has been developed combining previous research findings and in-house experience followed by statistical analysis of the results. The analysis was performed by dividing the wind turbine into assemblies and the failures events in severity categories. Depending on the failure severity category a different statistical methodology was applied, examining the reliability growth and the applicability of the “bathtub curve” concept for wind turbine reliability analysis. Finally, a methodology for adapting the results of the statistical analysis to site-specific environmental conditions is proposed.
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Sammouri, Wissam. « Data mining of temporal sequences for the prediction of infrequent failure events : application on floating train data for predictive maintenance ». Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1041/document.
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De nos jours, afin de répondre aux exigences économiques et sociales, les systèmes de transport ferroviaire ont la nécessité d'être exploités avec un haut niveau de sécurité et de fiabilité. On constate notamment un besoin croissant en termes d'outils de surveillance et d'aide à la maintenance de manière à anticiper les défaillances des composants du matériel roulant ferroviaire. Pour mettre au point de tels outils, les trains commerciaux sont équipés de capteurs intelligents envoyant des informations en temps réel sur l'état de divers sous-systèmes. Ces informations se présentent sous la forme de longues séquences temporelles constituées d'une succession d'événements. Le développement d'outils d'analyse automatique de ces séquences permettra d'identifier des associations significatives entre événements dans un but de prédiction d'événement signant l'apparition de défaillance grave. Cette thèse aborde la problématique de la fouille de séquences temporelles pour la prédiction d'événements rares et s'inscrit dans un contexte global de développement d'outils d'aide à la décision. Nous visons à étudier et développer diverses méthodes pour découvrir les règles d'association entre événements d'une part et à construire des modèles de classification d'autre part. Ces règles et/ou ces classifieurs peuvent ensuite être exploités pour analyser en ligne un flux d'événements entrants dans le but de prédire l'apparition d'événements cibles correspondant à des défaillances. Deux méthodologies sont considérées dans ce travail de thèse: La première est basée sur la recherche des règles d'association, qui est une approche temporelle et une approche à base de reconnaissance de formes. Les principaux défis auxquels est confronté ce travail sont principalement liés à la rareté des événements cibles à prédire, la redondance importante de certains événements et à la présence très fréquente de "bursts". Les résultats obtenus sur des données réelles recueillies par des capteurs embarqués sur une flotte de trains commerciaux permettent de mettre en évidence l'efficacité des approches proposéesIn order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time flow of data, called floating train data, consisting of georeferenced events, along with their spatial and temporal coordinates. Once ordered with respect to time, these events can be considered as long temporal sequences which can be mined for possible relationships. This has created a neccessity for sequential data mining techniques in order to derive meaningful associations rules or classification models from these data. Once discovered, these rules and models can then be used to perform an on-line analysis of the incoming event stream in order to predict the occurrence of target events, i.e, severe failures that require immediate corrective maintenance actions. The work in this thesis tackles the above mentioned data mining task. We aim to investigate and develop various methodologies to discover association rules and classification models which can help predict rare tilt and traction failures in sequences using past events that are less critical. The investigated techniques constitute two major axes: Association analysis, which is temporal and Classification techniques, which is not temporal. The main challenges confronting the data mining task and increasing its complexity are mainly the rarity of the target events to be predicted in addition to the heavy redundancy of some events and the frequent occurrence of data bursts. The results obtained on real datasets collected from a fleet of trains allows to highlight the effectiveness of the approaches and methodologies used
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Hussin, Burairah. « Development of a state prediction model to aid decision making in condition based maintenance ». Thesis, University of Salford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490430.
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Condition monitoring and fault diagnosis for operational equipment are developing and bowing their potential for enhancing the effectiveness and efficiency of maintenance management, including maintenance decision-making. In this thesis, our aim is to model the condition of equipment items subject to condition-monitoring in order to provide a quantitative measure to aid maintenance decision-making. A key ingredient towards dealing with the modelling work is to define the state or condition of the equipment with an appropriate measure and the observed condition monitoring may be a function of the state or condition of the operational equipment concerned. This leads to the two elements that are important in our modelling development; the need to develop a model that describes the system condition subject to its monitoring data and a decision model that is based upon the predicted system condition.
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Croker, John. « A methodology for the prediction of maintenance and support of fleets of repairable systems ». Thesis, University of Exeter, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370016.
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Sasaki, Sho. « Development and Validation of a Clinical Prediction Rule for Bacteremia among Maintenance Hemodialysis Patients in Outpatient Settings ». Kyoto University, 2017. http://hdl.handle.net/2433/226778.
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Yang, Lei. « Methodology of Prognostics Evaluation for Multiprocess Manufacturing Systems ». University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298043095.
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Iyengar, Nikhil. « Development of prediction models to measure vendor performance in surveillance and auditing of aircraft maintenance ». Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1181669133/.
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Kählert, Alexander [Verfasser], Uwe [Akademischer Betreuer] Klingauf et Joachim [Akademischer Betreuer] Metternich. « Specification and Evaluation of Prediction Concepts in Aircraft Maintenance / Alexander Kählert ; Uwe Klingauf, Joachim Metternich ». Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2017. http://d-nb.info/1129359336/34.
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Shelly, Aaron. « An Adaptive Recipe Compensation Approach for Enhanced Health Prediction in Semiconductor Manufacturing ». University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511793532937998.
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Baringoldz, Gregg Michael. « Cognitive factors in the prediction of outcome and maintenance in smoking cessation programs : a discriminant analysis ». Virtual Press, 1989. http://liblink.bsu.edu/uhtbin/catkey/720146.
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This study investigated the relationship between smoking cessation and cognitive factors of attributional style, self-efficacy and locus of control. The roles of examined as they contributed to the prediction of smoking status. Questionnaires designed to measure these cognitivevariables, were administered to participants of smoking cessation programs at two times during the study; immediately before participation in the smoking cessation program and immediately after completion of the program. Smoking status was assessed at these times, as well as via telephone twice after the program's completion. Subjects were obtained from 16 American Cancer Society smoking cessation clinics in the Southern California area. The results of stepwise discriminant analyses of variance successfully predicted smoking status at end-of-clinic and follow-up periods, using a combination of demographic, smoking behavior and cognitive predictors. Cross-validations of the predictive models also were able to predict smoking status at end-of-clinic and follow-up. Additional analyses included stepwise discriminant demographic and smoking behavior variables styles, as well as demographic and smoking behavior Cognitive Factors 5 analyses of participants who relapsed at follow-up, and a comparison of those who completed the program versus those who dropped out prematurely. Both analyses resulted in obtaining significant discriminant functions. A final analysis compared pre- and post-treatment responses on the cognitive measures via a 2 X 2 multiple analysis of variance (group X time). A significant interaction between group and time of assessment was obtained.Department of Counseling Psychology and Guidance Services
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Zhou, Yifan. « Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model ». Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/41696/1/Yifan_Zhou_Thesis.pdf.
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Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.
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Santos, William O. « An analysis of the prediction accuracy of the U.S. Navy repair turn-around time forecast model ». Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FSantos.pdf.
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Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2003.Thesis advisor(s): Robert A. Koyak, Samuel E. Buttrey. Includes bibliographical references (p. 55). Also available online.
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Alsyouf, Imad. « Cost Effective Maintenance for Competitve Advantages ». Doctoral thesis, Växjö universitet, Institutionen för teknik och design, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-394.
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This thesis describes the role of cost effective maintenance in achieving competitive advantages. It explores by means of a survey which maintenance practices are used, and how maintenance policies are selected in Swedish industries. Also, it suggests a model for selecting the most cost effective maintenance policy, and how to improve the effectiveness of condition based maintenance decision-making. Finally it discusses how to assess the impact of maintenance practices on business strategic objectives. The main results achieved in the thesis are 1) A better understanding of maintenance organisation, management, systems and maintenance status in Swedish industry. For example, it was found that about 70% of Swedish companies still consider maintenance as a cost centre. Preventive and predictive maintenance approaches are also emphasised. 2) Most Swedish firms, i.e. about 81%, use the accumulated knowledge and experience within the company as a method for maintenance selection. Besides, about 31% use a method based on modelling the time to failure and optimisation. About 10% use failure mode effect and criticality analysis (FMECA) and decision trees and only 2% use multiple criterion decision-making (MCDM). However, the most used maintenance selection method is not the one most satisfactory to its users. Furthermore, about 30% use a combination of at least two methods. 3) A practical model for selecting and improving the most cost effective maintenance policy was developed. It is characterised by incorporating all the strengths of the four methods used in industry. 4) A mechanistic model for predicting the value of vibration level was verified both at the lab and in a case study. 5) A model for identifying, assessing, monitoring and improving the economic impact of maintenance was developed and tested in a case study. Thus it was proved that maintenance is no longer a cost centre, but could be a profit-generating function. To achieve competitive advantages, companies should do the right thing, e.g. use the most cost effective maintenance policy, and they should do it right, e.g. ensure that they have the right competence. Furthermore, they should apply the never-ending improvement cycle, i.e. Plan-Do-Check-Act, which requires identifying problem areas by assessing the savings and profits generated by maintenance and monitoring the economic impact of the applied maintenance policy. Thus, they would know where investments should be allocated to eliminate the basic reasons for losses and increase savings. The major conclusion is that proper maintenance would improve the quality, efficiency and effectiveness of production systems, and hence enhances company competitiveness, i.e. productivity and value advantages, and long-term profitability.
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Martello, Rosanna. « Cloud storage and processing of automotive Lithium-ion batteries data for RUL prediction ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Trouver le texte intégralRésumé :
Lithium-ion batteries are the ideal choice for electric and hybrid vehicles, but the high cost and the relatively short life represent an open issue for automotive industries. For this reason, the estimation of battery Remaining Useful Life (RUL) and the State of Health (SoH) are primary goals in the automotive sector. Cloud computing provides all the resources necessary to store, process and analyze all sensor data coming from connected vehicles in order to develop Predictive Maintenance tasks. This project describes the work done during my internship at FEV Italia s.r.l. The aims were designing an architecture for managing the data coming from a vehicle fleet and developing algorithms able to predict the SoH and the RUL of Lithium-ion batteries. The designed architecture is based on three Amazon Web Services: Amazon Elastic Compute Cloud, Amazon Simple Storage Service and Amazon Relational Database Service. After data processing and the feature extraction, the RUL and SoH estimations are performed by training two Neural Networks.
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Wilcox, Susan E. « Improving the Definition of Exercise Maintenance : Evaluation of Concepts Related to Adherence ». Thesis, University of North Texas, 2002. https://digital.library.unt.edu/ark:/67531/metadc3195/.
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Physical activity has been demonstrated in the literature as an effective way to reduce the risk for development of chronic disease. The Transtheoretical Model (TTM) of behavior change has been developed as a means to predict and facilitate movement into healthier lifestyle behaviors. The model is centered on "stages of change", which describe a continuum of readiness to engage in a health behavior change. Stages contain temporal, qualitative, and quantitative characteristics. This was a six-month study that evaluated the effectiveness of stage-matched (theorized to be pertaining only to the maintenance stage of change) vs. generic (theorized to be pertaining to anyone, regardless of stage) newsletters in assisting subjects to attain the Maintenance stage of change. It also sought to identify further qualitative characteristics that can differentiate between the Action and Maintenance stages of change. Results indicated that monthly stage-matched newsletters were no more effective in helping subjects reaching Maintenance than were the generic newsletters. Exerciser self-schema was related to stages of change, but those relationships differed from baseline to six-month follow-up, indicating development of exerciser self-schema during the study period. Implications of this are discussed. Other concepts discussed included "structure" of change process, in that three new scores were developed and correlated with self-efficacy as well as intercorrelated. Motivation was also evaluated and compared across levels of success at adhering to exercise during a three-month period. Limitations of the study and implications are discussed.
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Sun, Yong. « Reliability prediction of complex repairable systems : an engineering approach ». Queensland University of Technology, 2006. http://eprints.qut.edu.au/16273/.
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This research has developed several models and methodologies with the aim of improving the accuracy and applicability of reliability predictions for complex repairable systems. A repairable system is usually defined as one that will be repaired to recover its functions after each failure. Physical assets such as machines, buildings, vehicles are often repairable. Optimal maintenance strategies require the prediction of the reliability of complex repairable systems accurately. Numerous models and methods have been developed for predicting system reliability. After an extensive literature review, several limitations in the existing research and needs for future research have been identified. These include the follows: the need for an effective method to predict the reliability of an asset with multiple preventive maintenance intervals during its entire life span; the need for considering interactions among failures of components in a system; and the need for an effective method for predicting reliability with sparse or zero failure data. In this research, the Split System Approach (SSA), an Analytical Model for Interactive Failures (AMIF), the Extended SSA (ESSA) and the Proportional Covariate Model (PCM), were developed by the candidate to meet the needs identified previously, in an effective manner. These new methodologies/models are expected to rectify the identified limitations of current models and significantly improve the accuracy of the reliability prediction of existing models for repairable systems. The characteristics of the reliability of a system will alter after regular preventive maintenance. This alternation makes prediction of the reliability of complex repairable systems difficult, especially when the prediction covers a number of imperfect preventive maintenance actions over multiple intervals during the asset's lifetime. The SSA uses a new concept to address this issue effectively and splits a system into repaired and unrepaired parts virtually. SSA has been used to analyse system reliability at the component level and to address different states of a repairable system after single or multiple preventive maintenance activities over multiple intervals. The results obtained from this investigation demonstrate that SSA has an excellent ability to support the making of optimal asset preventive maintenance decisions over its whole life. It is noted that SSA, like most existing models, is based on the assumption that failures are independent of each other. This assumption is often unrealistic in industrial circumstances and may lead to unacceptable prediction errors. To ensure the accuracy of reliability prediction, interactive failures were considered. The concept of interactive failure presented in this thesis is a new variant of the definition of failure. The candidate has made several original contributions such as introducing and defining related concepts and terminologies, developing a model to analyse interactive failures quantitatively and revealing that interactive failure can be either stable or unstable. The research results effectively assist in avoiding unstable interactive relationship in machinery during its design phase. This research on interactive failures pioneers a new area of reliability prediction and enables the estimation of failure probabilities more precisely. ESSA was developed through an integration of SSA and AMIF. ESSA is the first effective method to address the reliability prediction of systems with interactive failures and with multiple preventive maintenance actions over multiple intervals. It enhances the capability of SSA and AMIF. PCM was developed to further enhance the capability of the above methodologies/models. It addresses the issue of reliability prediction using both failure data and condition data. The philosophy and procedure of PCM are different from existing models such as the Proportional Hazard Model (PHM). PCM has been used successfully to investigate the hazard of gearboxes and truck engines. The candidate demonstrated that PCM had several unique features: 1) it automatically tracks the changing characteristics of the hazard of a system using symptom indicators; 2) it estimates the hazard of a system using symptom indicators without historical failure data; 3) it reduces the influence of fluctuations in condition monitoring data on hazard estimation. These newly developed methodologies/models have been verified using simulations, industrial case studies and laboratory experiments. The research outcomes of this research are expected to enrich the body of knowledge in reliability prediction through effectively addressing some limitations of existing models and exploring the area of interactive failures.
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Sun, Yong. « Reliability prediction of complex repairable systems : an engineering approach ». Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16273/1/Yong_Sun_Thesis.pdf.
Texte intégralRésumé :
This research has developed several models and methodologies with the aim of improving the accuracy and applicability of reliability predictions for complex repairable systems. A repairable system is usually defined as one that will be repaired to recover its functions after each failure. Physical assets such as machines, buildings, vehicles are often repairable. Optimal maintenance strategies require the prediction of the reliability of complex repairable systems accurately. Numerous models and methods have been developed for predicting system reliability. After an extensive literature review, several limitations in the existing research and needs for future research have been identified. These include the follows: the need for an effective method to predict the reliability of an asset with multiple preventive maintenance intervals during its entire life span; the need for considering interactions among failures of components in a system; and the need for an effective method for predicting reliability with sparse or zero failure data. In this research, the Split System Approach (SSA), an Analytical Model for Interactive Failures (AMIF), the Extended SSA (ESSA) and the Proportional Covariate Model (PCM), were developed by the candidate to meet the needs identified previously, in an effective manner. These new methodologies/models are expected to rectify the identified limitations of current models and significantly improve the accuracy of the reliability prediction of existing models for repairable systems. The characteristics of the reliability of a system will alter after regular preventive maintenance. This alternation makes prediction of the reliability of complex repairable systems difficult, especially when the prediction covers a number of imperfect preventive maintenance actions over multiple intervals during the asset's lifetime. The SSA uses a new concept to address this issue effectively and splits a system into repaired and unrepaired parts virtually. SSA has been used to analyse system reliability at the component level and to address different states of a repairable system after single or multiple preventive maintenance activities over multiple intervals. The results obtained from this investigation demonstrate that SSA has an excellent ability to support the making of optimal asset preventive maintenance decisions over its whole life. It is noted that SSA, like most existing models, is based on the assumption that failures are independent of each other. This assumption is often unrealistic in industrial circumstances and may lead to unacceptable prediction errors. To ensure the accuracy of reliability prediction, interactive failures were considered. The concept of interactive failure presented in this thesis is a new variant of the definition of failure. The candidate has made several original contributions such as introducing and defining related concepts and terminologies, developing a model to analyse interactive failures quantitatively and revealing that interactive failure can be either stable or unstable. The research results effectively assist in avoiding unstable interactive relationship in machinery during its design phase. This research on interactive failures pioneers a new area of reliability prediction and enables the estimation of failure probabilities more precisely. ESSA was developed through an integration of SSA and AMIF. ESSA is the first effective method to address the reliability prediction of systems with interactive failures and with multiple preventive maintenance actions over multiple intervals. It enhances the capability of SSA and AMIF. PCM was developed to further enhance the capability of the above methodologies/models. It addresses the issue of reliability prediction using both failure data and condition data. The philosophy and procedure of PCM are different from existing models such as the Proportional Hazard Model (PHM). PCM has been used successfully to investigate the hazard of gearboxes and truck engines. The candidate demonstrated that PCM had several unique features: 1) it automatically tracks the changing characteristics of the hazard of a system using symptom indicators; 2) it estimates the hazard of a system using symptom indicators without historical failure data; 3) it reduces the influence of fluctuations in condition monitoring data on hazard estimation. These newly developed methodologies/models have been verified using simulations, industrial case studies and laboratory experiments. The research outcomes of this research are expected to enrich the body of knowledge in reliability prediction through effectively addressing some limitations of existing models and exploring the area of interactive failures.
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Vitorino, Inês Patrícia Canelas. « Análise de dados de manutenção : estimação de probabilidade de falhas ». Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14730.
Texte intégralRésumé :
Mestrado em Métodos Quantitativos para a Decisão Económica e EmpresarialO presente trabalho resulta de uma parceria entre o ISEG e a empresa PSE - Produtos e Serviços de Estatística, Lda., tendo por base a integração num projeto sob a forma de estágio. Baseia-se no desenvolvimento de modelos analíticos para a gestão da manutenção de um cliente da PSE, isto é, na análise e identificação de padrões e comportamentos de um conjunto de ativos de modo a conseguir determinar, de forma antecipada, a necessidade de serviços de manutenção. O estudo e a previsão de ocorrências de manutenção tem uma importância central para a redução de custos, a disponibilidade dos ativos e, consequentemente, a produção. Mais especificamente, o projeto prende-se com a análise de dados de manutenção na área hospitalar. Para desenvolvimento do projeto, foram disponibilizados dados de manutenção relativos ao ano de 2016, nomeadamente dados do inventário dos ativos, custos de manutenção, manutenções corretivas e preventivas que foram realizadas. O projeto foi dividido em duas fases: Preparação e exploração dos dados - com o objetivo de descrever e caracterizar estatisticamente os principais indicadores e potenciais associações na manutenção; Modelização - com o objetivo de criar um modelo que permita conjugar tanto as condições intrínsecas aos equipamentos, como o seu histórico de manutenção e intervenções e as suas condições atuais, por forma a identificar indicadores avançados de possibilidade de falha. Posteriormente haverá a implementação dos resultados, que corresponderá a implementação técnica do modelo preditivo no sistema do cliente.
The present master's thesis is the result of a partnership between ISEG and the company PSE - Produtos e Serviços de Estatística, Lda., and it was developed based on the integration of a six-month internship project. This internship subject is to develop analytical models for the management of the maintenance of one of PSE's customers by analysing and identifying patterns and behaviours of a set of assets in order to determine, in advance, the need for maintenance services. The study and prediction of maintenance needs is crucial to achieve costs reduction, assets availability and, consequently, production. More specifically, the project deals with the analysis of maintenance data in the hospital field. For the development of this project, maintenance data for the year 2016 were made available, namely data on assets inventory, maintenance costs and corrective and preventive maintenance measures that were performed. The project was divided into two parts: Setting and analysation of data - with the aim to describe and determine the main indicators and potencial associations in the maintenance; Modeling - with the aim to create a model that allows the association of the primary condition of the equipment, its maintenance history, past interventions and its current conditions, in order to identify advanced indicators of the chance of failure. Subsequently, the results will be implemented, which will correspond to the technical implementation of the predictive model in the customer system.
info:eu-repo/semantics/publishedVersion
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Cao, Qiushi. « Semantic technologies for the modeling of predictive maintenance for a SME network in the framework of industry 4.0 Smart condition monitoring for industry 4.0 manufacturing processes : an ontology-based approach Using rule quality measures for rule base refinement in knowledge-based predictive maintenance systems Combining chronicle mining and semantics for predictive maintenance in manufacturing processes ». Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR04.
Texte intégralRésumé :
Dans le domaine de la fabrication, la détection d’anomalies telles que les défauts et les défaillances mécaniques permet de lancer des tâches de maintenance prédictive, qui visent à prévoir les défauts, les erreurs et les défaillances futurs et à permettre des actions de maintenance. Avec la tendance de l’industrie 4.0, les tâches de maintenance prédictive bénéficient de technologies avancées telles que les systèmes cyberphysiques (CPS), l’Internet des objets (IoT) et l’informatique dématérialisée (cloud computing). Ces technologies avancées permettent la collecte et le traitement de données de capteurs qui contiennent des mesures de signaux physiques de machines, tels que la température, la tension et les vibrations. Cependant, en raison de la nature hétérogène des données industrielles, les connaissances extraites des données industrielles sont parfois présentées dans une structure complexe. Des méthodes formelles de représentation des connaissances sont donc nécessaires pour faciliter la compréhension et l’exploitation des connaissances. En outre, comme les CPSs sont de plus en plus axées sur la connaissance, une représentation uniforme de la connaissance des ressources physiques et des capacités de raisonnement pour les tâches analytiques est nécessaire pour automatiser les processus de prise de décision dans les CPSs. Ces problèmes constituent des obstacles pour les opérateurs de machines qui doivent effectuer des opérations de maintenance appropriées. Pour relever les défis susmentionnés, nous proposons dans cette thèse une nouvelle approche sémantique pour faciliter les tâches de maintenance prédictive dans les processus de fabrication. En particulier, nous proposons quatre contributions principales: i) un cadre ontologique à trois niveaux qui est l’élément central d’un système de maintenance prédictive basé sur la connaissance; ii) une nouvelle approche sémantique hybride pour automatiser les tâches de prédiction des pannes de machines, qui est basée sur l’utilisation combinée de chroniques (un type plus descriptif de modèles séquentiels) et de technologies sémantiques; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) une nouvelle approche d’affinement de la base de règles qui utilise des mesures de qualité des règles comme références pour affiner une base de règles dans un système de maintenance prédictive basé sur la connaissance. Ces approches ont été validées sur des ensembles de données réelles et synthétiquesIn the manufacturing domain, the detection of anomalies such as mechanical faults and failures enables the launching of predictive maintenance tasks, which aim to predict future faults, errors, and failures and also enable maintenance actions. With the trend of Industry 4.0, predictive maintenance tasks are benefiting from advanced technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), and Cloud Computing. These advanced technologies enable the collection and processing of sensor data that contain measurements of physical signals of machinery, such as temperature, voltage, and vibration. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. Therefore formal knowledge representation methods are required to facilitate the understanding and exploitation of the knowledge. Furthermore, as the CPSs are becoming more and more knowledge-intensive, uniform knowledge representation of physical resources and reasoning capabilities for analytic tasks are needed to automate the decision-making processes in CPSs. These issues bring obstacles to machine operators to perform appropriate maintenance actions. To address the aforementioned challenges, in this thesis, we propose a novel semantic approach to facilitate predictive maintenance tasks in manufacturing processes. In particular, we propose four main contributions: i) a three-layered ontological framework that is the core component of a knowledge-based predictive maintenance system; ii) a novel hybrid semantic approach to automate machinery failure prediction tasks, which is based on the combined use of chronicles (a more descriptive type of sequential patterns) and semantic technologies; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) a novel rule base refinement approach that uses rule quality measures as references to refine a rule base within a knowledge-based predictive maintenance system. These approaches have been validated on both real-world and synthetic data sets
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Nasseri, Sahand. « Application of an Improved Transition Probability Matrix Based Crack Rating Prediction Methodology in Florida’s Highway Network ». Scholar Commons, 2008. https://scholarcommons.usf.edu/etd/424.
Texte intégralRésumé :
With the growing need to maintain roadway systems for provision of safety and comfort for travelers, network level decision-making becomes more vital than ever. In order to keep pace with this fast evolving trend, highway authorities must maintain extremely effective databases to keep track of their highway maintenance needs. Florida Department of Transportation (FDOT), as a leader in transportation innovations in the U.S., maintains Pavement Condition Survey (PCS) database of cracking, rutting, and ride information that are updated annually.
Crack rating is an important parameter used by FDOT for making maintenance decisions and budget appropriation. By establishing a crack rating threshold below which traveler comfort is not assured, authorities can screen the pavement sections which are in need of Maintenance and Rehabilitation (M&R). Hence, accurate and reliable prediction of crack thresholds is essential to optimize the rehabilitation budget and manpower. Transition Probability Matrices (TPM) can be utilized to accurately predict the deterioration of crack ratings leading to the threshold. Such TPMs are usually developed by historical data or expert or experienced maintenance engineers' opinion. When historical data are used to develop TPMs, deterioration trends have been used vii indiscriminately, i.e. with no discrimination made between pavements that degrade at different rates. However, a more discriminatory method is used in this thesis to develop TPMs based on classifying pavements first into two groups. They are pavements with relatively high traffic and, pavements with a history of excessive degradation due to delayed rehabilitation.
The new approach uses a multiple non-linear regression process to separately optimize TPMs for the two groups selected by prior screening of the database. The developed TPMs are shown to have minimal prediction errors with respect to crack ratings in the database that were not used in the TPM formation. It is concluded that the above two groups are statistically different from each other with respect to the rate of cracking. The observed significant differences in the deterioration trends would provide a valuable tool for the authorities in making critical network-level decisions. The same methodology can be applied in other transportation agencies based on the corresponding databases.
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Mohammadisohrabi, Ali. « Design and implementation of a Recurrent Neural Network for Remaining Useful Life prediction ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Trouver le texte intégralRésumé :
A key idea underlying many Predictive Maintenance solutions is Remaining Useful Life (RUL) of machine parts, and it simply involves a prediction on the time remaining before a machine part is likely to require repair or replacement. Nowadays, with respect to fact that the systems are getting more complex, the innovative Machine Learning and Deep Learning algorithms can be deployed to study the more sophisticated correlations in complex systems. The exponential increase in both data accumulation and processing power make the Deep Learning algorithms more desirable that before. In this paper a Long Short-Term Memory (LSTM) which is a Recurrent Neural Network is designed to predict the Remaining Useful Life (RUL) of Turbofan Engines. The dataset is taken from NASA data repository. Finally, the performance obtained by RNN is compared to the best Machine Learning algorithm for the dataset.
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Nguyen, Hoang-Phuong. « Model-based and data-driven prediction methods for prognostics ». Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASC021.
Texte intégralRésumé :
La dégradation est un phénomène inévitable qui affecte les composants et les systèmes d'ingénierie, et qui peut entraîner leurs défaillances avec des conséquences potentiellement catastrophiques selon l'application. La motivation de cette Thèse est d'essayer de modéliser, d'analyser et de prédire les défaillances par des méthodes pronostiques qui peuvent permettre une gestion prédictive de la maintenance des actifs. Cela permettrait aux décideurs d'améliorer la planification de la maintenance, augmentant ainsi la disponibilité et la sûreté du système en minimisant les arrêts imprévus. Dans cet objectif, la recherche au cours de la thèse a été consacrée à l'adaptation et à l'utilisation d'approches basées sur des modèles et d'approches pilotées par les données pour traiter les processus de dégradation qui peuvent conduire à différents modes de défaillance dans les composants industriels, en utilisant différentes sources d'informations et de données pour effectuer des prédictions sur l'évolution de la dégradation et estimer la durée de vie utile restante (RUL).Les travaux de thèse ont porté sur deux applications pronostiques spécifiques: les pronostics basés sur des modèles pour la prédiction de la croissance des fissures par fatigue et les pronostics pilotées par les données pour les prédictions à pas multiples des données de séries chronologiques des composants des Centrales Nucléaires.Les pronostics basé sur des modèles compter sur le choix des modèles adoptés de Physics-of-Failure (PoF). Cependant, chaque modèle de dégradation ne convient qu'à certains processus de dégradation dans certaines conditions de fonctionnement, qui souvent ne sont pas connues avec précision. Pour généraliser, des ensembles de multiples modèles de dégradation ont été intégrés dans la méthode pronostique basée sur les modèles afin de tirer profit des différentes précisions des modèles spécifiques aux différentes dégradations et conditions. Les principales contributions des approches pronostiques proposées basées sur l'ensemble des modèles sont l'intégration d'approches de filtrage, y compris le filtrage Bayésien récursif et le Particle Filtering (PF), et de nouvelles stratégies d'ensemble pondérées tenant compte des précisions des modèles individuels dans l'ensemble aux étapes de prédiction précédentes. Les méthodes proposées ont été validées par des études de cas de croissance par fissures de fatigue simulées dans des conditions de fonctionnement variables dans le temps.Quant à la prédictions à pas multiples, elle reste une tâche difficile pour le Prognostics and Health Management (PHM) car l'incertitude de prédiction a tendance à augmenter avec l'horizon temporel de la prédiction. La grande incertitude de prédiction a limité le développement de pronostics à pas multiples dans les applications. Pour résoudre le problème, de nouveaux modèles de prédiction à pas multiples basés sur la Long Short-Term Memory (LSTM), un réseau de neurones profond développé pour traiter les dépendances à long terme dans les données de séries chronologiques, ont été développés dans cette Thèse. Pour des applications pratiques réalistes, les méthodes proposées abordent également les problèmes supplémentaires de détection d'anomalie, d'optimisation automatique des hyper-paramètres et de quantification de l'incertitude de prédiction. Des études de cas pratiques ont été envisagées, concernant les données de séries chronologiques collectées auprès des Générateurs de Vapeur et de Pompes de Refroidissement de Réacteurs de Centrales NucléairesDegradation is an unavoidable phenomenon that affects engineering components and systems, and which may lead to their failures with potentially catastrophic consequences depending on the application. The motivation of this Thesis is trying to model, analyze and predict failures with prognostic methods that can enable a predictive management of asset maintenance. This would allow decision makers to improve maintenance planning, thus increasing system availability and safety by minimizing unexpected shutdowns. To this aim, research during the Thesis has been devoted to the tailoring and use of both model-based and data-driven approaches to treat the degradation processes that can lead to different failure modes in industrial components, making use of different information and data sources for performing predictions on the degradation evolution and estimating the Remaining Useful Life (RUL).The Ph.D. work has addressed two specific prognostic applications: model-based prognostics for fatigue crack growth prediction and data-driven prognostics for multi-step ahead predictions of time series data of Nuclear Power Plant (NPP) components.Model-based prognostics relies on the choice of the adopted Physics-of-Failure (PoF) models. However, each degradation model is appropriate only to certain degradation process under certain operating conditions, which are often not precisely known. To generalize this, ensembles of multiple degradation models have been embedded in the model-based prognostic method in order to take advantage of the different accuracies of the models specific to different degradations and conditions. The main contributions of the proposed ensemble of models-based prognostic approaches are the integration of filtering approaches, including recursive Bayesian filtering and Particle Filtering (PF), and novel weighted ensemble strategies considering the accuracies of the individual models in the ensemble at the previous time steps of prediction. The proposed methods have been validated by case studies of fatigue crack growth simulated with time-varying operating conditions.As for multi-step ahead prediction, it remains a difficult task of Prognostics and Health Management (PHM) because prediction uncertainty tends to increase with the time horizon of the prediction. Large prediction uncertainty has limited the development of multi-step ahead prognostics in applications. To address the problem, novel multi-step ahead prediction models based on Long Short- Term Memory (LSTM), a deep neural network developed for dealing with the long-term dependencies in the time series data have been developed in this Thesis. For realistic practical applications, the proposed methods also address the additional issues of anomaly detection, automatic hyperparameter optimization and prediction uncertainty quantification. Practical case studies have been considered, concerning time series data collected from Steam Generators (SGs) and Reactor Coolant Pumps (RCPs) of NPPs
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Nguyen, Danh Ngoc. « Contribution aux approches probabilistes pour le pronostic et la maintenance des systèmes contrôlés ». Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0010/document.
Texte intégralRésumé :
Les systèmes de contrôle-commande jouent un rôle important dans le développement de la civilisation et de la technologie moderne. La perte d’efficacité de l’actionneur agissant sur le système est nocive dans le sens où elle modifie le comportement du système par rapport à celui qui est désiré. Cette thèse est une contribution au pronostic de la durée de vie résiduelle (RUL) et à la maintenance des systèmes de contrôle-commande en boucle fermée avec des actionneurs soumis à dégradation. Dans une première contribution, un cadre de modélisation à l'aide d’un processus markovien déterministe par morceaux est considéré pour modéliser le comportement du système. Dans ce cadre, le comportement du système est représenté par des trajectoires déterministes qui sont intersectées par des sauts d'amplitude aléatoire se produisant à des instants aléatoires et modélisant le phénomène de dégradation discret de l'actionneur. La deuxième contribution est une méthode de pronostic de la RUL du système composée de deux étapes : estimation de la loi de probabilité de l'état du système à l'instant de pronostic par le filtre particulaire et calcul de la RUL qui nécessite l'estimation de la fiabilité du système à partir de cet instant. La troisième contribution correspond à la proposition d’une politique de maintenance à structure paramétrique permettant de prendre en compte dynamiquement les informations disponibles conjointement sur l'état et sur l'environnement courant du système et sous la contrainte de dates d'opportunitéThe automatic control systems play an important role in the development of civilization and modern technology. The loss of effectiveness of the actuator acting on the system is harmful in the sense that it modifies the behavior of the system compared to that desired. This thesis is a contribution to the prognosis of the remaining useful life (RUL) and the maintenance of closed loop systems with actuators subjected to degradation. In the first contribution, a modeling framework with piecewise deterministic Markov process is considered in order to model the overall behavior of the system. In this context, the behavior of the system is represented by deterministic trajectories that are intersected by random size jumps occurring at random times and modeling the discrete degradation phenomenon of the actuator. The second contribution is a prognosis method of the system RUL which consists of two steps: the estimation of the probability distribution of the system state at the prognostic instant by particle filtering and the computation of the RUL which requires the estimation of the system reliability starting from the prognostic instant. The third contribution is the proposal of a parametric maintenance policy which dynamically take into account the available information on the state and on the current environment of the system and under the constraint of opportunity dates
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Daher, Alaa. « Diagnostic et pronostic des défauts pour la maintenance préventive et prédictive. Application à une colonne de distillation ». Thesis, Normandie, 2018. http://www.theses.fr/2018NORMR090/document.
Texte intégralRésumé :
Le procédé de distillation est largement utilisé dans de nombreuses applications telles que la production pétrochimique, le traitement du gaz naturel, les raffineries de pétrole, etc. Généralement, la maintenance des réacteurs chimiques est très coûteuse et perturbe la production pendant de longues périodes. Tous ces facteurs démontrent réellement la nécessité de stratégies efficaces de diagnostic et de pronostic des défauts pour pouvoir réduire et éviter le plus grand nombre de ces problèmes catastrophiques. La première partie de nos travaux vise à proposer une méthode de diagnostic fiable pouvant être utilisée dans le régime permanent d’une procédure non linéaire. De plus, nous proposons une procédure modifiée de la méthode MFCM permettant de calculer la variation en pourcentage entre deux classes. L’utilisation de MFCM a pour objectif de réduire le temps de calcul et d’accroître les performances du classifieur. Les résultats de la méthode proposée confirment la capacité de classifier entre les différentes classes de défaillances considérées. Le calcul de la durée de vie du système est extrêmement important pour éviter les pannes catastrophiques. Notre deuxième objectif est de proposer une méthode fiable de pronostic permettant d’estimer le chemin de dégradation d’une colonne de distillation et de calculer le pourcentage de durée de vie de ce système. Le travail présente une approche basée sur le système d’inférence neuro-fuzzy adaptatif (ANFIS) combiné avec (FCM) pour prédire la trajectoire future et calculer le pourcentage de durée de vie du système. Les résultats obtenus démontrent la validité de la technique proposée pour atteindre les objectifs requis avec une précision de haut niveau. Pour améliorer les performances d’ANFIS, nous proposons la distribution de Parzen comme nouvelle fonction d’appartenance de l’algorithme ANFIS. Les résultats ont démontré l’importance de la technique proposée car elle s’est avérée efficace pour réduire le temps de calcul. En outre, la distribution de Parzen présentait la plus petite erreur quadratique moyenne (RMSE). La dernière partie de cette thèse se concentrait sur la proposition d’un nouvel algorithme pouvant être appliqué pour obtenir un système de surveillance en temps réel s’appuyant sur la prédiction de défauts ; cela signifie que cette méthode permet de prédire l’état futur du système, puis de diagnostiquer quelle est la source d’erreur probable. Elle permet d’évaluer la dégradation d’une colonne de distillation et de diagnostiquer par la suite les défauts ou accidents pouvant survenir à la suite de la dégradation estimée. Cette nouvelle approche combine les avantages d’ANFIS à ceux de RNA permettant d’atteindre un haut niveau de précisionThe distillation process is largely used in many applications such a petrochemical production, natural gas processing, and petroleum refineries, etc. Usually, maintenance of the chemical reactors is very costly and it disrupts production for long periods of time. All these factors really demonstrate the fundamental need for effective fault diagnosis and prognostic strategies that they are able to reduce and avoid the greatest number of thes problems and disasters. The first part of our work aims to propose a reliable diagnostic method that can be used in the steady-state regime of a nonlinear procedure. Moreover, we propose a modified procedure of the fuzzy c-means clustering method (MFCM) where MFCM calculates the percentage variation between the two clustered classes. The purpose of using MFCM is to reduce the computing time and increase the performance of the classifier. The results of the proposed method confirm the ability to classify between normal mode and eight abnormal modes of faults. Our second goal aims to propose a prognosis reliable method used to estimate the degradation path of a distillation column and calculate the lifetime percentage of this system. The work presents an approach based on adaptive neuro-fuzzy inference system (ANFIS) combined with (FCM) to predict the future path and calculate the lifetime percentage of the system. The results obtained demonstrate the validity of the proposed technique to achieve the needed objectives with a high-level accuracy. To improve ANFIS performance we propose Parzen windows distribution as a new membership function for ANFIS algorithm. Results demonstrated the importance of the proposed technique since it proved to be highly successful in terms of reducing the time consumed. Additionally, Parzen windows had the smallest Root Mean Square Error (RMSE). The last part of this thesis was focusing on the proposing of new algorithm which can be applied to obtain real-time monitoring system which relies on the fault production module to reach the diagnosis module in contrast to the previous strategies ; this means this method predict the future state of the system then diagnosis what is the probable fault source. This proposed method has proven to be a reliable process that can evaluate the degradation of a distillation column and subsequently diagnose the possible faults or accidents that can emerge as a result of the estimated degradation. This new approach combines the benefits of ANFIS with the benefits of feedforward ANN. The results were demonstrated that the technique achieved with a high level of accuracy, the objective of prediction and diagnosis especially when applied to the data obtained from automated distillation process in the chemical industry
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Nguyen, Kim Anh. « Développement de stratégies de maintenance prévisionnelle de systèmes multi-composants avec structure complexe ». Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0027/document.
Texte intégralRésumé :
Aujourd'hui, les systèmes industriels deviennent de plus en plus complexes. Cette complexité est due d’une part à la structure du système qui ne se résume pas à des structures classiques en fiabilité, d’autre part à la prise en compte de composants présentant des phénomènes de dégradation graduelle que des systèmes de monitoring permettent de surveiller. Ceci mène à l'objectif de cette thèse portant sur le développement des stratégies de maintenance prévisionnelle pour des systèmes multi-composants complexes. Les politiques envisagées proposent notamment des stratégies de regroupement de composants permettant de tirer des dépendances économiques identifiées. Des facteurs d'importance permettant de prendre en compte la structure du système et la dépendance économique sont développés et combinés avec les évaluations de fiabilité prévisionnelle des composants pour l’élaboration de règles de décision de regroupement. De plus, un couplage des règles de décision de maintenance et de gestion des stocks est également étudié. L’ensemble des études menées montrent l’intérêt de la prise en compte de la fiabilité prévisionnelle des composants, des dépendances économiques et de la structure complexe du système dans l'aide à la décision de maintenance et de gestion des stocks. L’avantage des stratégies développées est vérifié en les comparant à d’autres existantes dans la littératureToday, industrial systems become more and more complex. The complexity is due partly to the structure of the system that cannot be reduced to classic structure reliability (series structures, parallel structures, series-parallel structures, etc), secondly the consideration of components with gradual degradation phenomena that can be monitored. This leads to the main purpose of this thesis on the development of predictive maintenance strategies for complex multi-component systems. The proposed policies provide maintenance grouping strategies to take advantage of the economic dependence between components. The predictive reliability of components and importance measures allowing taking into account the structure of the system and economic dependence are developed to construct the grouping decision rules. Moreover, a joint decision rule for maintenance and spare parts provisioning is also studied.All the conducted studies show the interest in the consideration of the predictive reliability of components, economic dependencies as well as complex structure of the system in maintenance decisions and spare parts provisioning. The advantage of the developed strategies is confirmed by comparing with the other existing strategies in the literature
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Lindell, David. « Process Mapping for Laser Metal Deposition of Wire using Thermal Simulations : A prediction of material transfer stability ». Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-85474.
Texte intégralRésumé :
Additive manufacturing (AM) is a quickly rising method of manufacturing due to its ability to increase design freedom. This allows the manufacturing of components not possible by traditional subtractive manufacturing. AM can greatly reduce lead time and material waste, therefore decreasing the cost and environmental impact. The adoption of AM in the aerospace industry requires strict control and predictability of the material deposition to ensure safe flights. The method of AM for this thesis is Laser Metal Deposition with wire (LMD-w). Using wire as a feedstock introduces a potential problem, the material transfer from the wire to the substrate. This requires all process parameters to be in balance to produce a stable deposition. The first sign of unbalanced process parameters are the material transfer stabilities; stubbing and dripping. Stubbing occurs when the energy to melt the wire is too low and the wire melts slower than required. Dripping occurs when too much energy is applied and the wire melts earlier than required. These two reduce the predictability and stability that is required for robust manufacturing. Therefore, the use of thermal simulations to predict the material transfer stability for LMD-w using Waspaloy as the deposition material has been studied. It has been shown that it is possible to predict the material transfer stability using thermal simulations and criterions based on preexisting experimental data. The criterion for stubbing checks if the completed simulation result produces a wire that ends below the melt pool. For dripping two criterions shows good results, the dilution ratio is a good predictor if the tool elevation remains constant. If there is a change in tool elevation the dimensionless slenderness number is a better predictor. Using these predictive criterions it is possible to qualitatively map the process window and better understand the influence of tool elevation and the cross-section of the deposited material.Additiv tillverkning (AT) är en kraftigt växande tillverkningsmetod på grund av sin flexibilitet kring design och möjligheten att skapa komponenter som inte är tillverkningsbara med traditionell avverkande bearbetning. AT kan kraftigt minska tid- och materialåtgång och på så sett minskas kostnader och miljöpåverkan. Införandet av AT i flyg- och rymdindustrin kräver strikt kontroll och förutsägbarhet av processen för att försäkra sig om säkra flygningar. Lasermetalldeponering av tråd är den AT metod som hanteras i denna uppsats. Användandet av tråd som tillsatsmaterial skapar ett potentiellt problem, materialöverföringen från tråden till substratet. Detta kräver att alla processparametrar är i balans för att få en jämn materialöverföring. Är processen inte balanserad syns detta genom materialöverföringsstabiliteterna stubbning och droppning. Stubbning uppkommer då energin som tillförs på tråden är för låg och droppning uppkommer då energin som tillförs är för hög jämfört med vad som krävs för en stabil process. Dessa två fenomen minskar möjligheterna för en kontrollerbar och stabil tillverkning. På grund av detta har användandet utav termiska simuleringar för att prediktera materialöverföringsstabiliteten för lasermetalldeponering av tråd med Waspaloy som deponeringsmaterial undersökts. Det har visat sig vara möjligt att prediktera materialöverföringsstabiliteten med användning av termiska simuleringar och kriterier baserat på tidigare experimentell data. Kriteriet för stubbning kontrolleras om en slutförd simulering resulterar i en tråd som når under smältan. För droppning finns två fungerande kriterier, förhållandet mellan svetshöjd och penetrationsdjup om verktygshöjden är konstant, sker förändringar i verktygshöjden är det dimensionslös ”slenderness” talet ett bättre kriterium. Genom att använda dessa kriterier är det möjligt att kvalitativt kartlägga processfönstret och skapa en bättre förståelse för förhållandet mellan verktygshöjden och den deponerade tvärsnittsarean.
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Dawoua, Kaoutoing Maxime. « Contributions à la modélisation et la simulation de la coupe des métaux : vers un outil d'aide à la surveillance par apprentissage ». Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0013.
Texte intégralRésumé :
Les procédés de mise en forme par enlèvement de matière, encore appelés usinage, sont les procédés de fabrication les plus utilisés pour la production des pièces mécaniques, notamment dans les secteurs industriels tels que l’aéronautique, l’automobile, le ferroviaire, etc. Bien que ces procédés soient largement utilisés dans l’industrie, la prédiction des grandeurs caractéristiques de l’usinage n’est pas toujours précise, et un mauvais choix des conditions de coupe peut être a l’origine de l’usure anormale de l’outil, voire de la dégradation de la qualité de la pièce usinée. La simulation fine des grandeurs de l’usinage, en vue de la détection des anomalies, est un bon exemple de cette problématique, car représentative du problème général d’optimisation de la coupe des métaux pour obtenir une précision de coupe et anticiper l’usure rapide des outils. Cette thèse est une contribution a la modélisation et a la simulation de la coupe des métaux, en vue d’une aide a la décision aux entreprises de fabrication de pièces mécaniques, basée sur l’extraction des connaissances a partir des données simulées. Une implémentation efficiente d'un modèle analytique de coupe orthogonale de métaux, capable de prédire les paramètres de coupe en un temps réduit a été proposée. La performance de ce modèle a été étudiée en comparant ses prédictions avec les données d’usinage de l’acier 1045 et de l’acier au carbone, disponibles dans la littérature. En exploitant la rapidité de la résolution obtenue à partir de l’implémentation proposée, une grande quantité de données simulant des conditions réelles de coupe a été générée, et a permis d’élaborer une démarche de surveillance de l’usinage, basée sur une méthode d’apprentissage profond non supervisée. La mise en œuvre avec les données simulées a permis de mettre en exergue la capacité de la démarche de détection proposée a identifier les combinaisons de valeurs des paramètres d’entrée (du modèle analytique de coupe) susceptibles de provoquer une température interne anormalement élevée ; celle-ci a en effet été considérée dans la thèse comme indicateur de santé du système d’usinage. L'implémentation du modèle d'apprentissage proposé a donné un taux de justesse de 99,96 % et une précision de 96 %, traduisant ainsi sa capacité à prédire efficacement le résultatShaping processes by material removal, also known as machining, are the manufacturing processes most commonly used for the production of mechanical parts, particularly in industrial sectors such as aeronautics, automotive, railways, etc. Although these processes are widely used in industry, the prediction of the characteristic sizes of the machining process is not always accurate, and a poor choice of cutting conditions can lead to abnormal tool wear or even to a deterioration in the quality of the machined part. The fine simulation of machining parameters, aiming at detecting anomalies, is a good example of this problem, as it represents the general problem of optimizing metal cutting to obtain cutting accuracy and anticipate rapid tool wear. This thesis is a contribution to the modelling and simulation of metal cutting, with a view to assisting mechanical parts manufacturing companies in their decision-making, based on knowledge extraction from simulated data. An efficient implementation of an analytical model of orthogonal cutting of metals, able to predict cutting parameters in a reduced time was proposed. The performance of this model was studied by comparing its predictions with the 1045 and carbon steel machining data that are available in the literature. By using the high speed resolution obtained from the proposed implementation, a large quantity of data simulating real cutting conditions was generated, and allowed the elaboration of a machining monitoring approach, based on a deep unsupervised learning method. The implementation with the simulated data highlighted the ability of the proposed detection approach to identify combinations of input parameter values (from the analytical cutting model) that could generate an abnormally high internal temperature; this was considered in the thesis as an indicator of the health of the machining system. Implementation of the proposed learning model gave an accuracy of 99,96 % and a precision of 96%, reflecting its ability to effectively predict the outcome
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Callow, Daniel John. « Optimisation of the Neural Network Process for an Improved Bridge Deterioration Model ». Thesis, Griffith University, 2015. http://hdl.handle.net/10072/367038.
Texte intégralRésumé :
Infrastructure maintenance is a vital aspect for any country to ensure safety and reliability of its infrastructure and the population which use these assets. To ensure that the highest degree of maintenance is performed and recorded for infrastructure, Bridge Management Systems (BMSs) have been developed to allow bridge agencies to have an effective means to determine and understand the best decisions to make for infrastructure maintenance.
Various models have been developed for the BMS with the most typical approach being the stochastic Markovian-based method, using currently retrieved bridge data as inputs for predicting the bridges’ future deterioration levels. However, a drawback to this method is the disregard for historical data as references to future predictions. This situation has led to the advancement of BMSs to incorporate Artificial Neural Network (ANN) processes as a means of predicting future bridge deterioration levels.
This advancement in ANN-based BMSs is an improvement over the typical model due to the incorporation of historical data curves. However, a drawback to this is the fact that biannual bridge inspection data has only started to be collected within the past 10-20 years, limiting the inputs for ANN methods. Further research into ANN models has developed a means of deriving the missing historical data through the use of current bridge inspection data and non-bridge data collected from various sources. This method is referred to as the Backwards-Prediction Model (BPM) and is an effective method for determining this missing historical data for subsequent use as inputs to further ANN methods for future prediction.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
Full Text
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Li, Jiawei M. Eng Massachusetts Institute of Technology. « A case model for predictive maintenance ». Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/43139.
Texte intégralRésumé :
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, February 2008.Includes bibliographical references (leaves 59-60).
This project is to respond to a need by Varian Semiconductor Equipment Associates, Inc. (VSEA) to help predict failure of ion implanters. Predictive maintenance would help to reduce the unscheduled downtime of ion implanters, whose throughput and uptime is highly important to customers. Statistical analysis is performed on historical data to extract metadata that can reflect the machine health, and statistical process control (SPC) is applied to detect deviations from normal or in-control behavior. Methods for failure prevention are also investigated. Challenging points in this project are the noise in raw signal data and the difference in data signals of different robots. To address these challenges, we apply signal filtering to extract cycle motions from raw data, and develop different generic as well as specific metadata extraction techniques for different robots. We test the extraction approaches and results using healthy data of ten machines, and find that the metadata on which we chose to perform SPC is suitable and can serve as a consistent indicator of a machine's health. We further develop an application using Visual Basic based on our study, and provide a user guide on how to generate the analysis reports on new data using our application.
by Jiawei Li.
M.Eng.
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Tyagi, Prakhar. « Chassis predictive maintenance and service solutions ». Thesis, KTH, Fordonsdynamik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265587.
Texte intégralRésumé :
Predictive Maintenance (PdM) accumulates data from multiple sensors developing a statistical model which identifies the key failures even before they take place. The main focus of this thesis work has been the proposal of a machine learning based system designed for predicting the failure of mechanical parts that require replacement. The main investigation explores the possibilities of implementing machine learning algorithm for predicting the parts that require replacement and which is found from the electronic errors that the vehicle exhibits. A strong association between the parts that cause faults and electronic error codes helps in yielding a powerful diagnostics tool. The study has considered three error components namely; broken damper, noisy wheel hub and the reference value for the validation purpose. The model vehicle used for the study is Volvo V90. To acquire variance in this study data, diverse tracks with different speeds were used. The machine learning algorithm that was developed can classify and detect mechanical failures using an Support Vector Machine (SVM) algorithm based on various statistical learning methods. The study carried out an fast Fourier transform (FFT) analysis in association with the data acquired from front left wheel. The main area of interest is the FFT domain of 5-20hz. The study outcome indicated that the used model is capable of predicting the hysteretic responses associated with the faulty components like broken damper and noisy wheel hub. The designed model can be used for analysing the system’s response and for designing and controlling the faulty components in the car. However, the results of this thesis work can be used to implement the time-based prediction of mechanical component decay.Prediktivt Underhåll (PdM) är en statistisk modell som samlar data från flera olika sensorer och som identifierar fel innan de äger rum. Huvudfokus för detta examensarbete har varit förslaget till ett maskininlärningsbaserat system som är utformat för att förutsäga fel i mekaniska delar som kräver utbyte. Examensarbetet undersöker möjligheterna att implementera en maskininlärningsalgoritm för att förutsäga de mekaniska delar som kräver utbyte och som framgår av de elektroniska fel som fordonet uppvisar. En stark koppling mellan de delar som orsakar fel och elektroniska felkoder hjälper till att ge ett kraftfullt diagnostiskt verktyg. Studien har beaktat tre felkomponenter nämligen; trasig dämpare, missljud från hjulnav och referensvärdet för valideringsändamål. Modellfordonet som används för studien är Volvo V90. För att få varians i informationen för detta arbete användes olika provbanor med olika vägförhållanden med olika hastigheter. Maskininlärningsalgoritmen som utvecklades kan klassificera och upptäcka mekaniska fel med hjälp av en SVM-algoritm (Support Vector Machine) baserad på olika statistiska inlärningsmetoder. Studien genomförde en snabb Fourier-transform (FFT) analys i samband med de data som förvärvades från det främre vänstra hjulet. Huvudintresseområdet är FFT-domänen 5-20 Hz. Studiens resultat visade att den använda modellen kan: Identifiera och klassificera data som är förknippade med de felaktiga komponenterna som trasig dämpare och missljud i hjulnav. Modellen kan användas för vidare prediktera och ge förslag när ett mekaniskt fel på dämpare eller hjulnav håller på att ske. Det här examensarbetet täcker inte tidsbunden prediktion utan snarare identifierar när nedbrytningen av mekaniska komponenter har skett. Resultaten från detta examensarbete kan emellertid användas för att implementera en tidsbaserad prediktion för mekaniska komponentfel.
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Korvesis, Panagiotis. « Machine Learning for Predictive Maintenance in Aviation ». Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX093/document.
Texte intégralRésumé :
L'augmentation des données disponibles dans presque tous les domaines soulève la nécessité d'utiliser des algorithmes pour l'analyse automatisée des données. Cette nécessité est mise en évidence dans la maintenance prédictive, où l'objectif est de prédire les pannes des systèmes en observant continuellement leur état, afin de planifier les actions de maintenance à l'avance. Ces observations sont générées par des systèmes de surveillance habituellement sous la forme de séries temporelles et de journaux d'événements et couvrent la durée de vie des composants correspondants. Le principal défi de la maintenance prédictive est l'analyse de l'historique d'observation afin de développer des modèles prédictifs.Dans ce sens, l'apprentissage automatique est devenu omniprésent puisqu'il fournit les moyens d'extraire les connaissances d'une grande variété de sources de données avec une intervention humaine minimale. L'objectif de cette thèse est d'étudier et de résoudre les problèmes dans l'aviation liés à la prévision des pannes de composants à bord. La quantité de données liées à l'exploitation des avions est énorme et, par conséquent, l'évolutivité est une condition essentielle dans chaque approche proposée.Cette thèse est divisée en trois parties qui correspondent aux différentes sources de données que nous avons rencontrées au cours de notre travail. Dans la première partie, nous avons ciblé le problème de la prédiction des pannes des systèmes, compte tenu de l'historique des Post Flight Reports. Nous avons proposé une approche statistique basée sur la régression précédée d'une formulation méticuleuse et d'un prétraitement / transformation de données. Notre méthode estime le risque d'échec avec une solution évolutive, déployée dans un environnement de cluster en apprentissage et en déploiement. À notre connaissance, il n'y a pas de méthode disponible pour résoudre ce problème jusqu'au moment où cette thèse a été écrite.La deuxième partie consiste à analyser les données du livre de bord, qui consistent en un texte décrivant les problèmes d'avions et les actions de maintenance correspondantes. Le livre de bord contient des informations qui ne sont pas présentes dans les Post Flight Reports bien qu'elles soient essentielles dans plusieurs applications, comme la prédiction de l'échec. Cependant, le journal de bord contient du texte écrit par des humains, il contient beaucoup de bruit qui doit être supprimé afin d'extraire les informations utiles. Nous avons abordé ce problème en proposant une approche basée sur des représentations vectorielles de mots. Notre approche exploite des similitudes sémantiques, apprises par des neural networks qui ont généré les représentations vectorielles, afin d'identifier et de corriger les fautes d'orthographe et les abréviations. Enfin, des mots-clés importants sont extraits à l'aide du Part of Speech Tagging.Dans la troisième partie, nous avons abordé le problème de l'évaluation de l'état des composants à bord en utilisant les mesures des capteurs. Dans les cas considérés, l'état du composant est évalué par l'ampleur de la fluctuation du capteur et une tendance à l'augmentation monotone. Dans notre approche, nous avons formulé un problème de décomposition des séries temporelles afin de séparer les fluctuations de la tendance en résolvant un problème convexe. Pour quantifier l'état du composant, nous calculons à l'aide de Gaussian Mixture Models une fonction de risque qui mesure l'écart du capteur par rapport à son comportement normalThe increase of available data in almost every domain raises the necessity of employing algorithms for automated data analysis. This necessity is highlighted in predictive maintenance, where the ultimate objective is to predict failures of hardware components by continuously observing their status, in order to plan maintenance actions well in advance. These observations are generated by monitoring systems usually in the form of time series and event logs and cover the lifespan of the corresponding components. Analyzing this history of observation in order to develop predictive models is the main challenge of data driven predictive maintenance.Towards this direction, Machine Learning has become ubiquitous since it provides the means of extracting knowledge from a variety of data sources with the minimum human intervention. The goal of this dissertation is to study and address challenging problems in aviation related to predicting failures of components on-board. The amount of data related to the operation of aircraft is enormous and therefore, scalability is a key requirement in every proposed approach.This dissertation is divided in three main parts that correspond to the different data sources that we encountered during our work. In the first part, we targeted the problem of predicting system failures, given the history of Post Flight Reports. We proposed a regression-based approach preceded by a meticulous formulation and data pre-processing/transformation. Our method approximates the risk of failure with a scalable solution, deployed in a cluster environment both in training and testing. To our knowledge, there is no available method for tackling this problem until the time this thesis was written.The second part consists analyzing logbook data, which consist of text describing aircraft issues and the corresponding maintenance actions and it is written by maintenance engineers. The logbook contains information that is not reflected in the post-flight reports and it is very essential in several applications, including failure prediction. However, since the logbook contains text written by humans, it contains a lot of noise that needs to be removed in order to extract useful information. We tackled this problem by proposing an approach based on vector representations of words (or word embeddings). Our approach exploits semantic similarities of words, learned by neural networks that generated the vector representations, in order to identify and correct spelling mistakes and abbreviations. Finally, important keywords are extracted using Part of Speech Tagging.In the third part, we tackled the problem of assessing the health of components on-board using sensor measurements. In the cases under consideration, the condition of the component is assessed by the magnitude of the sensor's fluctuation and a monotonically increasing trend. In our approach, we formulated a time series decomposition problem in order to separate the fluctuation from the trend by solving a convex program. To quantify the condition of the component, we compute a risk function which measures the sensor's deviation from it's normal behavior, which is learned using Gaussian Mixture Models
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Williamsson, Ia. « Total Quality Maintenance (TQMain) A predictive and proactive maintenance concept for software ». Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2281.
Texte intégralRésumé :
This thesis describes an investigation of the possibility to apply a maintenance concept originally developed for the industry, on software maintenance. Today a large amount of software development models exist but not many of them treat maintenance as a part of the software life cycle. In most cases maintenance is depicted as an activity towards the end of the software life cycle. The high cost ascribed to software maintenance motivates for improvements. The maintenance concept TQMain proposed in this thesis distinguishes from other maintenance concepts by its use of preventive, predictive and proactive maintenance strategies. TQMain uses a common database to store real-time data from various departments and uses it for analyse and assessment to track the development of deviations in the condition of the production process and product quality at an early stage. A continuous cyclic improvement of the maintenance strategy is reached by comparing the data from the real-time measurements with data from the database. The ISO/IEC Software engineering – Product qualities is used as a source of empiric data to conclude that the correct quality characteristics are used for identifying software product quality and its characteristics and compare them with the characteristics of industrial product quality. The results presented are that in the conceptual outline of TQMain measures are obviously not the same as in software maintenance, but the aspect of product quality is common for both. The continuous cyclic improvement of the product quality that TQMain features together with the aspect of detecting potential failures before they occur would, judging from the conceptual outline of TQMain be applicable on software maintenance.
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Uunk, Florian. « A New Perspective on Predicting Maintenance Costs ». Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-14610.
Texte intégralRésumé :
In my thesis I focus on providing a foundation of data on whichdecision makers can base refactoring decisions. For this, I examine therelationship between software complexity and maintenance eort. Tomake the data a stronger basis for refactoring decisions, I present anew approach of correlating le metrics to maintenance eort, whereI look at the relation between changes in le metrics over multiplereleases and changes in the maintenance eort spent on these les. Ido this using a broadened and, more complete notion of maintenanceeort. I measure maintenance eort in 4 ways: the amount of lines ofcode that had to be changed to resolve tasks, the amount of discus-sion that tasks generated, the amount of atomic changes to a le thatwere required to resolve a task, and the amount of bugs per month.To test this framework, I extracted data from 3 open source projects,where I measured the variation of both complexity and maintenanceeort, using this new notion of eort, over multiple releases, and in-vestigated their correlation. I found that 21 of the tested metrics weresignicantly correlated to the eort measures, where complexity basedmetrics and incoming propagation cost show the highest correlation.Of the proposed measures for maintenance eort, the amount of dis-cussion to resolve an issue shows the highest correlation to the chosenmetrics.
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Karlsson, Lotta. « Predictive Maintenance for RM12 with Machine Learning ». Thesis, Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42283.
Texte intégralRésumé :
Few components within mechanical engineering possess the fatigue resistance as of high-pressure turbine blades found in jet engines. This as they are designed to perform in extensively high temperatures under severe loading which causes degradation to be an important aspect despite a design, optimized for its environment. This study aims to find a method for predicting life consumption of those blades belonging to the turbine section of the jet engine in JAS 39 Gripen C/D called RM12. This was performed at GKN Aerospace, which holds the military type certificate for this engine as well as a patented solution that determines life consumption in components depending on operational history. With the help of machine learning in Matlab, flight sensor data and loading results, the method was to explore a variety of prediction models and find a selection of blades with varied utilization before reaching end of life for comparison. Followed by a search of understanding the life limiting fatigue conditions and the factors involved in the deterioration process. A similarity finding approach gave valuable meaning to the accuracy of regression analysis from flight data towards output in form of temperature predictions. Comparing known and reliable fatigue calculation results gave however no clear picture as inspected blades had reach their limit at very diverse accumulated values. The next approach was therefore to investigate if an initialization point of degradation could be found, from where the result could give an answer that matched for all blades and their different utilization. The result was that an accelerated degradation after high loading could give a prediction that could explain the total life consumption with an accuracy of 87% for 19 out of 21 investigated blades. The accelerated deterioration could in theory be explained by the fact that the fatigue resistance as well as different types of degradation, propagates each other and originates from thermal loading making them all contributors, whereas the conventional numerical methods only handles them separately. In order to get confidence, valuable and reliable predictions, the models do however need to be accompanied with more testing and adding of contributing factors before assumed as a proven method for life consumption determination of the high-pressure turbine blades.
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Sedghi, Mahdieh. « Data-driven predictive maintenance planning and scheduling ». Licentiate thesis, Luleå tekniska universitet, Industriell Ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80828.
Texte intégralRésumé :
The railway track network is one of the major modes of transportation and among a country’s most valuable infrastructure assets. Maintenance and renewal of railway infrastructure have a vital role in safety performance, the quality of the ride, train punctuality, and the life cycle cost of assets. Due to the large proportion of maintenance costs, increasing the efficiency of maintenance through optimised planning can result in high amounts of cost-saving. Moreover, from a safety perspective, late maintenance intervention can result in defective track and rollingstock components, which in severe cases, can cause severe accidents such as derailments. An effective maintenance management system is required to ensure the availability of the infrastructure system and meet the increasing capacity demand. The recent rapid technological revolution and increasing deployment of sensors and connected devices created new possibilities to increase the maintenance strategy effectiveness in the railway network. The purpose of this thesis is to expand the knowledge and methods for planning and scheduling of railway infrastructure maintenance. The research vision is to find quantitative approaches for integrated tactical planning and operational scheduling of predictive condition-based maintenance which can be put to practical use and improve the efficiency of the railway system. First, a thorough literature review study is performed to identify improvement policies for maintenance planning and scheduling in the literature and also to analyse the current approaches in optimising the maintenance planning and scheduling problem. Second, a novel data-driven multi-level decision-making framework to improve the efficiency of maintenance planning and scheduling is developed. The proposed framework aims to support the selection of track segments for maintenance by providing a practical degradation prediction model based on available condition measurement data. The framework considers the uncertainty of future predictions using the probability of surpassing a maintenance limit instead of using the predicted value. Moreover, an extensive total maintenance cost formulation is developed to include both direct and indirect preventive and corrective costs to observe the effect of using cost optimisation and grouping algorithms at the operational scheduling level. The performance of the proposed framework is evaluated through a case study based on data from a track section of the iron ore line between Boden and Luleå. The results indicate that the proposed approach can lead to cost savings in both optimal and grouping plans. This framework may be a useful decision support tool in the automated planning and scheduling of maintenance based on track geometry measurements.
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Killeen, Patrick. « Knowledge-Based Predictive Maintenance for Fleet Management ». Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40086.
Texte intégralRésumé :
In recent years, advances in information technology have led to an increasing number of devices (or things) being connected to the internet; the resulting data can be used by applications to acquire new knowledge. The Internet of Things (IoT) (a network of computing devices that have the ability to interact with their environment without requiring user interaction) and big data (a field that deals with the exponentially increasing rate of data creation, which is a challenge for the cloud in its current state and for standard data analysis technologies) have become hot topics. With all this data being produced, new applications such as predictive maintenance are possible. One such application is monitoring a fleet of vehicles in real-time to predict their remaining useful life, which could help companies lower their fleet management costs by reducing their fleet's average vehicle downtime. Consensus self-organized models (COSMO) approach is an example of a predictive maintenance system for a fleet of public transport buses, which attempts to diagnose faulty buses that deviate from the rest of the bus fleet. The present work proposes a novel IoT-based architecture for predictive maintenance that consists of three primary nodes: namely, the vehicle node (VN), the server leader node (SLN), and the root node (RN). The VN represents the vehicle and performs lightweight data acquisition, data analytics, and data storage. The VN is connected to the fleet via its wireless internet connection. The SLN is responsible for managing a region of vehicles, and it performs more heavy-duty data storage, fleet-wide analytics, and networking. The RN is the central point of administration for the entire system. It controls the entire fleet and provides the application interface to the fleet system. A minimally viable prototype (MVP) of the proposed architecture was implemented and deployed to a garage of the Soci\'et\'e de Transport de l'Outaouais (STO), Gatineau, Canada. The VN in the MVP was implemented using a Raspberry Pi, which acquired sensor data from a STO hybrid bus by reading from a J1939 network, the SLN was implemented using a laptop, and the RN was deployed using meshcentral.com. The goal of the MVP was to perform predictive maintenance for the STO to help reduce their fleet management costs.
The present work also proposes a fleet-wide unsupervised dynamic sensor selection algorithm, which attempts to improve the sensor selection performed by the COSMO approach. I named this algorithm the improved consensus self-organized models (ICOSMO) approach. To analyze the performance of ICOSMO, a fleet simulation was implemented. The J1939 data gathered from a STO hybrid bus, which was acquired using the MVP, was used to generate synthetic data to simulate vehicles, faults, and repairs. The deviation detection of the COSMO and ICOSMO approach was applied to the synthetic sensor data. The simulation results were used to compare the performance of the COSMO and ICOSMO approach. Results revealed that in general ICOSMO improved the accuracy of COSMO when COSMO was not performing optimally; that is, in the following situations: a) when the histogram distance chosen by COSMO was a poor choice, b) in an environment with relatively high sensor white noise, and c) when COSMO selected poor sensors. On average ICOSMO only rarely reduced the accuracy of COSMO, which is promising since it suggests deploying ICOSMO as a predictive maintenance system should perform just as well or better than COSMO . More experiments are required to better understand the performance of ICOSMO. The goal is to eventually deploy ICOSMO to the MVP.
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Aguilar, Fredy Armando Aguilar. « Modelagem matemática da eficiência de utilização da energia e da proteína dietéticas pelo pacu (Piaractus mesopotamicus Holmberg, 1887) ». Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11139/tde-02052016-103450/.
Texte intégralRésumé :
Sistemas intensivos de produção de peixes demandam o uso de dietas completas e manejo alimentar e nutricional precisos. O principal objetivo da nutrição dos peixes é melhorar a eficiência da utilização da energia e dos nutrientes da ração. Alta eficiência, na realidade, significa maior retenção de nutrientes e energia, i.e., maior crescimento, com menor descarga de nutrientes nos corpos d\'água. O objetivo deste trabalho foi estudar a eficiência de utilização da energia e a proteína da ração para o pacu, Piaractus mesopotamicus. Em um primeiro ensaio foram caracterizadas as propriedades físicoquímicas e os coeficientes de digestibilidade aparente da proteína e da energia de 28 amostras de rações para peixes onívoros comercializadas na região de Piracicaba, estado de São Paulo. Os dados obtidos foram utilizados no ajuste de modelos de regressão linear múltipla para predizer os conteúdos de energia digestível (ED) e proteína digestível (PD) das rações comerciais para a espécie. Uum segundo grupo de ensaios foi dedicado ao estudo da eficiência metabólica da utilização da energia e da proteína. Utilizando-se a técnica de respirometria de fluxo intermitente, foi quantificada a taxa metabólica padrão em peixes de diferente tamanho (17 g - 1050 g) em cinco temperaturas (19, 23, 26, 29 e 33°C). O coeficiente oxi-calórico para oxidação de gordura (13,72 J mg-1 O2) foi utilizado para converter os dados de consumo de oxigênio em taxas de produção de calor. O coeficiente alométrico da produção de calor em jejum foi próximo a 0,8, valor usual para outras espécies de peixes. A partir da aplicação do método fatorial de análise foram estimadas as exigências de energia digestível e proteína digestível para mantença e para o crescimento do pacu e o efeito do nível de lipídeos dietéticos (alto - AL, ou baixo - BL) sobre as exigências nutricionais. O nível de lipídeos da ração não influenciou a estimativa de exigência de energia para mantença (26,57 kJ de ED kg-0,8 dia-1 e 0,076 g de PD kg-0,7 dia-1). A exigência de energia digestível para crescimento (kJ de ED por kJ de energia retida) foi maior para a ração BL (1,387) do que para a ração AL (1,285). A exigência em proteína digestível (g de PD por g de proteína depositada) foi maior para peixes alimentados com a ração BL do que com a ração AL (1,7015 vs. 1,583).Intensive fish farming systems entail the use of complete feeds and accurate feeding and nutrition management. The main objective of fish feeding and nutrition practices is the efficient use of feed energy and nutrients. High efficiency actually means increased retention of nutrients and energy, i.e., improved growth ratio and reduced discharge of nutrients in the water. This work aimed at studying the efficiency of use of feed energy and protein of pacu, Piaractus mesopotamicus. A first trial characterized physicochemical properties and apparent digestibility coefficients of protein and energy of 28 commercial, omnivorous fish feeds sampled in the region of Piracicaba, state of São Paulo. The data were used to set multiple linear regression models predicting the digestible energy (DE) and digestible protein (DP) contents of commercial, sampled feeds. A second group of trials studied the metabolic efficiency of use of energy and protein. The intermittent flow respirometry technique was used to quantify the standard metabolic rate of different fish size classes (17 g - 1050 g) at five temperatures (19, 23, 26, 29 and 33 ° C). The coefficient for oxy-caloric fat oxidation (13.72 J mg-1 O2) was used to convert the oxygen consumption data to heat production ratios. The allometric coefficient of heat production in fasting condition was 0.8, a typical value for other fish species. Digestible energy and digestible protein requirements for maintenance and growth and effects of dietary lipids (high - AL, or low - BL) contents on nutritional requirements of pacu were then studied with the aid of factorial analysis method. Dietary lipid contents did not affect energy requirements for maintenance (26.57 kJ DE kg-0.8 day-1 and 0.076 g DP kg-0.7 day-1). The digestible energy requirement for growth (kJ of ED per kJ of energy retained) was higher for BL feeds (1.387) than for AL feeds (1.285). The requirements of digestible protein (g DP per g of deposited protein) was higher for the BL than for the AL feed (1.7015 vs. 1.583).
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Westerlund, Per. « Condition measuring and lifetime modelling of disconnectors, circuit breakers and other electrical power transmission equipment ». Doctoral thesis, KTH, Elektroteknisk teori och konstruktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214984.
Texte intégralRésumé :
The supply of electricity is important in modern society, so the outages of the electric grid should be few and short, especially for the transmission grid. A summary of the history of the Swedish electrical system is presented. The objective is to be able to plan the maintenance better by following the condition of the equipment. The risk matrix can be used to choose which component to be maintained. The risk matrix is improved by adding a dimension, the uncertainty of the probability. The risk can be reduced along any dimension: better measurements, preventive maintenance or more redundancy. The number of dimensions can be reduced to two by following iso-risk lines calculated for the beta distribution. This thesis lists twenty surveys about circuit breakers and disconnectors, with statistics about the failures and the lifetime. It also presents about forty condition-measuring methods for circuit breakers and disconnectors, mostly applicable to the electric contacts and the mechanical parts. A method for scheduling thermography based on analysis of variance of the current is tried. Its aim is to reduce the uncertainty of thermography and it is able to explain two thirds of the variation using the time of the day, the day of the week and the week number as explanatory variables. However, the main problem remains as the current is in general too low. A system with IR sensors has been installed at the nine contacts of six disconnectors with the purpose of avoiding outages for maintenance if the contacts are in a good condition. The measured temperatures are sent by radio and regressed against the square of the current, the best exponent found. The coefficient of determination $R^2$ is high, greater than 0.9. The higher the regression coefficient is, the more heat is produced at the contact. So this ranks the different contacts. Finally a framework for lifetime modelling and condition measuring is presented. Lifetime modelling consists in associating a distribution of time to failure with each subpopulation. Condition measuring means measuring a parameter and estimating its value in the future. If it exceeds a threshold, maintenance should be carried out. The effect of maintenance of the contacts is shown for four disconnectors. An extension of the risk matrix with uncertainty, a survey of statistics and condition monitoring methods, a system with IR sensors at contacts, a thermography scheduling method and a framework for lifetime modelling and condition measuring are presented. They can improve the planning of outages for maintenance. Finally a framework for lifetime modelling and condition measuring is presented. Lifetime modelling consists in associating a distribution of time to failure with each subpopulation. Condition measuring means measuring a parameter and estimating its value in the future. If it exceeds a threshold, maintenance should be carried out. The effect of maintenance of the contacts is shown for four disconnectors. An extension of the risk matrix with uncertainty, a survey of statistics and condition monitoring methods, a system with IR sensors at contacts, a thermography scheduling method and a framework for lifetime modelling and condition measuring are presented. They can improve the planning of outages for maintenance.Elförsörjningen är viktig i det moderna samhället, så avbrotten bör vara få och korta, särskilt i stamnätet. En kortfattad historik över det svenska elsystemet presenteras. Målet är att kunna planera avbrotten för underhåll bättre genom att veta mera om apparaternas skick. Det är svårt att planera avbrott för underhåll och utbyggnad. Riskmatrisen är verktyg för att välja vad som ska underhållas och den kan förbättras genom att lägga till en dimension, sannolikhetens osäkerhet. Risken kan minskas längs med varje dimension: bättre mätningar, förebyggande underhåll och mer redundans. Antalet dimensioner kan igen bli två genom att följa linjer med samma risk, som är beräknade för betafördelningen. Denna avhandling tar upp tjugo studier av fel i brytare och frånskiljare med data om felorsak och livslängd. Den har också en översikt av ett fyrtiotal olika metoder för tillståndsmätningar för brytare och frånskiljare, som huvudsakligen rör de elektriska kontakterna och de mekaniska delarna. Ett system med IR sensorer har installerats på de nio kontakterna på sex frånskiljare. Målet är att minska antalet avbrott för underhåll genom att skatta skicket när frånskiljarna är i drift. De uppmätta temperaturerna tas emot genom radio och behandlas genom regression mot kvadraten av strömmen, då den bästa exponenten för strömmen visade sig vara 2,0. Förklaringsfaktorn $R^2$ är hög, över 0,9. För varje kontakt ger det en regressionskoefficient. Ju högre koefficienten är, desto mer värme utvecklas det i kontakten, vilket kan leda till skador på materialet. Koefficienterna ger en rangordning av frånskiljarna. Systemet kan också användas för att minska eller öka den tillåtna strömmen baserat på skicket. Slutligen förklaras ett ramverk för livslängdsmodellering och tillståndsmätning. Livslängdsmodellering innebär att koppla en fördelning för tiden till fel med varje delpopulation. Med tillståndsmätning avses att mäta en parameter och skatta dess värde i framtiden. Om den överskrider en tröskel, måste apparaten underhållas. Effekten av underhåll visas för fyra frånskiljare. En utveckling av riskmatrisen med osäkerheten, en sammanställning av statistik och metoder för tillståndsövervakning, ett system med IR-sensor vid kontakerna, en metod för termografiplanering och ett ramverk för livslängdsmodellering och tillståndsmätningar presenteras. De kan förbättra avbrottsplaneringen.
El suministro de energía eléctrica es importante en la sociedad moderna. Por eso los cortes eléctricos deben ser poco frecuentes y de poca duración, sobre todo en la red de transmisión. Esta tesis resume la historia del sistema eléctrico sueco. El objetivo es planificar los cortes mejor siguiendo la condición de los aparatos. La matriz de riesgo se utiliza muchas veces para escoger en qué aparatos debería realizarse mantenimiento. Esta matriz se puede mejorar añadiendo una dimensión: la incertidumbre de la probabilidad. El riesgo puede ser disminuido siguiendo cada una de las tres dimensiones: mejores mediciones, mantenimiento preventivo y mayor redundancia. El número de dimensiones puede reducirse siguiendo líneas del mismo riesgo calculadas para la distribución beta. Esta tesis presenta veinte estudios de fallos en interruptores y seccionadores con datos sobre la causa y el tiempo hasta la avería. Contiene también una visión general de cuarenta métodos para medir la condición de seccionadores e interruptores, aplicables en su mayoría a los contactos eléctricos y los componentes mecánicos. Se ha instalado un sistema con sensores infrarrojos en los seis contactos de nueve seccionadores. El objetivo es disminuir los cortes de servicio para mantenimiento, estimando la condición con el seccionador en servicio. Las temperaturas son transmitidas por radio y se hace una regresión con el cuadrado de la corriente, ya que el mejor exponente de la corriente resultó ser 2,0. $R^2$ alcanza un valor de 0,9 indicando un buen ajuste de los datos por parte del modelo. Existe un coeficiente de regresión para cada contacto y este sirve para ordenar los contactos según la necesidad de mantenimiento, ya que cuanto mayor sea el coeficiente más calor se produce en el contacto. Finalmente se explica que el modelado de tiempo hasta la avería consiste en asignar una distribución estadística a cada equipo. La monitorización del estado consiste en medir y estimar un parámetro y luego predecir su valor en el futuro. Si va a sobrepasar un cierto límite, el equipo necesitará de mantenimiento. Se presenta el efecto de mantenimiento de cuatro seccionadores. Un desarrollo de la matriz de riesgo, un conjunto de estadísticas y métodos de monitoreo de condición, un sistema de sensores IR situados cerca de los contactos, en método de planificación de termografía y un concepto para explicar la modelización de tiempo hasta la avería y de la monitorización de la condición han sido presentados y hace posible una mejor planificación de los cortes de servicio.
QC 20170928
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Chávez, Gómez Víctor Hugo. « Sistema de información para el control, seguimiento y mantenimiento del equipamiento hospitalario ». Bachelor's thesis, Universidad Ricardo Palma, 2010. http://cybertesis.urp.edu.pe/handle/urp/44.
Texte intégralRésumé :
The main purpose of this research is to present a solution that enable to manage efficient and
reliable way, all of the information in relation to control, tracking and the hospital equipment
maintenance. So, was taken as an object of study of Engineering Department of the Central
Hospital of the Air Force of Peru, which presents a lot of administrative deficiencies character in
its internal processes of reception, record and closing of Work Orders as well as the preventive
and corrective maintenance of the hospital equipment of the HCFAP.The contemplated solution
comprises from analysis and design to the development of some use cases more significant of the application.El presente trabajo de investigación tiene como propósito fundamental presentar una solución que permita administrar de forma eficiente y confiable toda la información respecto al control, seguimiento y mantenimiento del equipamiento hospitalario. Para ello se tomó como objeto de estudio al Departamento de Ingeniería del Hospital Central de la Fuerza Aérea del Perú, el cual presenta muchas deficiencias de carácter administrativo en sus procesos internos de recepción, registro y cierre de Órdenes de Trabajo así como el mantenimiento preventivo y correctivo de los equipos hospitalarios del HCFAP. La solución contemplada abarca desde el análisis y diseño hasta el desarrollo de algunos casos de uso más significativos de la aplicación.
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Pryor, Jacqueline. « Earthwork maintenance : a geotechnical database and predictive model ». Thesis, Cardiff University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266614.
Texte intégral
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De, Giorgi Marcello. « Tree ensemble methods for Predictive Maintenance : a case study ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22282/.
Texte intégralRésumé :
Nel lavoro descritto in questa tesi sono stati creati modelli per la manutenzione predittiva di macchine utensili in ambito industriale; in particolare, i modelli realizzati sono stati addestrati sfruttando degli ensemble tree methods con le finalità di: predire il verificarsi di un guasto in macchina con un anticipo tale da permettere l'organizzazione delle squadre di manutenzione; predire la necessità della sostituzione anticipata dell'utensile utilizzato dalla macchina, per mantenere alti gli standard di qualità. Dopo aver dato uno sfondo al contesto industriale in esame, la tesi illustra i processi seguiti per la creazione e l'aggregazione di un dataset, e l'introduzione di informazioni relative agli eventi in macchina. Analizzato il comportamento di alcune variabili durante la lavorazione ed effettuata una distinzione tra cicli di lavorazione validi e non validi, si procede introducendo gli ensemble tree methods e il motivo della scelta di questa classe di algoritmi. Nel dettaglio, vengono presentati due possibili candidati al problema trattato: Random Forest ed XGBoost; dopo averne descritto il funzionamento, vengono presentati i risultati ottenuti dai modelli proponendo, per stimarne l'efficacia, un funzione di costo atteso come alternativa all'accuracy score. I risultati dei modelli allenati con i due algoritmi proposti vengono infine confrontati.
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FURTADO, FELIPE MIANA DE FARIA. « NEURAL NETWORKS FOR PREDICTIVE MAINTENANCE ON OFF-HIGWAY TRUCKS ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=15673@1.
Texte intégralRésumé :
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCom o aumento da demanda por minério no mundo, a complexidade, o tamanho e o preço dos equipamentos de extração mineral aumentaram consideravelmente. Como estas máquinas possuem uma tecnologia de monitoramento embarcada no equipamento, a utilização desses dados para o aumento da confiabilidade e da disponibilidade do equipamento tornou-se fundamental, de modo a reduzir os custos de manutenção. O objetivo desta dissertação foi desenvolver um modelo de apoio à decisão de parada de equipamento, baseado na classificação por Redes Neurais Artificiais de padrões pré-falha de caminhões fora de estrada. O modelo proposto tem como objetivo identificar o estado de falha, ou padrão pré-falha de um equipamento, utilizando os dados armazenados nos equipamentos e seus respectivos registros de falha, para que seja possível avaliar o risco de falha deste equipamento e decidir se o mesmo deve ser parado ou aguardar uma nova parada programada. Essa dissertação foi desenvolvida em quatro partes: estudo dos principais modelos de manutenção atualmente utilizados; definição e desenvolvimento do modelo para abordar o problema, baseado em redes neurais artificiais; avaliação de desempenho do modelo proposto; e simulação do downtime da máquina utilizando o modelo de decisão proposto. No estudo dos principais modelos foi realizada uma pesquisa bibliográfica sobre a evolução da manutenção, passando por modelos de manutenção corretiva, manutenção preventiva e, por fim, chegando ao modelo de manutenção baseada no monitoramento de condições. Para os dois últimos tipos de manutenção, foram apresentados os principais modelos utilizados na abordagem do problema, seus benefícios e deficiências. O desenvolvimento do modelo foi segmentado em três etapas principais: tratamento das bases de dados, tanto de dados obtidos diretamente do equipamento quanto das bases de registro de falha dos equipamentos; seleção de variáveis, baseada no cálculo da influência de cada sensor do equipamento na determinação de seu estado de falha, assim como na definição do intervalo ideal para se agrupar os dados; e definição da topologia das redes. Na etapa de avaliação do desempenho do modelo proposto foram utilizados dados de falhas corretivas mais recorrentes para os dois componentes específicos de caminhões fora de estrada: motor e transmissão, sendo que o monitoramento eletrônico do motor é mais extenso do que o de transmissão, no que diz respeito ao número de sensores empregados no monitoramento. Para a comparação de desempenho entre os diferentes modelos avaliados, dois fatores tiveram maior relevância: melhor desempenho na classificação e maior intervalo entre a identificação do padrão pré-falha e a ocorrência da falha. Os resultados de classificação dos padrões pré-falha foram bastante satisfatórios para a maioria dos casos de estudos, com as taxas de acerto variando entre 85% e 95%. A partir do modelo de classificação determinado na etapa anterior, passou-se à simulação de diferentes cenários de falhas, calculando-se os tempos de máquina parada (downtimes) que teriam sido evitados se as intervenções definidas pelo modelo tivessem sido executadas, analisando-se, assim, o aumento de disponibilidade proporcionado pelo uso do modelo proposto.
With the increasing demand for ore in the world, the complexity, size and price of mining equipment have increased considerably. As these machines have embedded monitoring technology, the use of such data to increase the reliability and availability of the equipment has become essential in order to reduce maintenance costs. The objective of this work is developing a model that supports the decision of stopping an equipment, based on its actual state, using pattern recognition by neural networks. The proposed model aims to identify the state of equipment failure or pre-failure based on the data stored in the equipment and on the records of failure, so as to assess the risk of failure of equipment and to decide whether it should be stopped or wait for a new programmed shutdown. This dissertation was developed in four parts: study of the main models currently used for maintenance; design and implementation of the model to address this problem, based on artificial neural networks; performance evaluation of the proposed model; and simulation of equipment downtime using the proposed model. In the study of the main models a research was made about the evolution of maintenance techniques, through models of corrective maintenance, preventive maintenance and, finally, reaching the maintenance model based on condition monitoring. For the last two types of maintenance, it is presented the main models used in addressing the problem, its benefits and shortcomings. The development of the model was segmented into three main stages: processing of databases, from the data obtained directly from the equipment to the base of record of equipment failure; variable selection, based on the calculation of the influence of each equipment sensor to determine its failure state, as well as the definition of the ideal range of group data, and definition of the topology of networks. In the stage of assessing the performance of the proposed model we used data from corrective failures more often of two specific components of off-highway trucks: engine and transmission. To compare the performance between the different models evaluated, two factors were more important: classification performance and the longest interval between the identification of a pre-failure pattern and the occurrence of the failure. The results of classification of pre-failure patterns were quite satisfactory for most case studies, with hit rates ranging between 85% and 96%. From the classification model given in the previous step, we moved on to simulate different failure scenarios, calculating the equipment downtime that would have been avoided if the interventions defined by the model had been implemented, thus analyzing the increased availability provided by the use of the proposed model.
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Gorman, Joe, Glenn Takata, Subhash Patel et Dan Grecu. « A Constraint-Based Approach to Predictive Maintenance Model Development ». International Foundation for Telemetering, 2008. http://hdl.handle.net/10150/606187.
Texte intégralRésumé :
ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, CaliforniaPredictive maintenance is the combination of inspection and data analysis to perform maintenance when the need is indicated by unit performance. Significant cost savings are possible while preserving a high level of system performance and readiness. Identifying predictors of maintenance conditions requires expert knowledge and the ability to process large data sets. This paper describes a novel use of constraint-based data-mining to model exceedence conditions. The approach extends the extract, transformation, and load process with domain aggregate approximation to encode expert knowledge. A data-mining workbench enables an expert to pose hypotheses that constrain a multivariate data-mining process.