Дисертації з теми "MAINTENANCE PREDICTION"
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Morrison, David J. "Prediction of software maintenance costs." Thesis, Edinburgh Napier University, 2001. http://researchrepository.napier.ac.uk/Output/3601.
Повний текст джерелаIshihara, Yasuo. "Prediction of human error in rail car maintenance." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10629.
Повний текст джерелаHartmann, 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.
Повний текст джерелаKumbala, 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.
Повний текст джерелаPodda, 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.
Повний текст джерелаTse, 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.
Повний текст джерелаWan, 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.
Повний текст джерелаKaidis, 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.
Повний текст джерелаSammouri, 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.
Повний текст джерелаIn 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
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.
Повний текст джерелаCroker, 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.
Повний текст джерелаSasaki, 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.
Повний текст джерелаYang, Lei. "Methodology of Prognostics Evaluation for Multiprocess Manufacturing Systems." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298043095.
Повний текст джерелаIyengar, 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/.
Повний текст джерелаKählert, Alexander [Verfasser], Uwe [Akademischer Betreuer] Klingauf, and 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.
Повний текст джерелаShelly, 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.
Повний текст джерелаBaringoldz, 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.
Повний текст джерелаDepartment of Counseling Psychology and Guidance Services
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.
Повний текст джерелаSantos, 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.
Повний текст джерелаThesis advisor(s): Robert A. Koyak, Samuel E. Buttrey. Includes bibliographical references (p. 55). Also available online.
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.
Повний текст джерелаMartello, Rosanna. "Cloud storage and processing of automotive Lithium-ion batteries data for RUL prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Знайти повний текст джерелаWilcox, 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/.
Повний текст джерелаSun, Yong. "Reliability prediction of complex repairable systems : an engineering approach." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16273/.
Повний текст джерелаSun, 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.
Повний текст джерелаVitorino, 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.
Повний текст джерелаO 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
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.
Повний текст джерелаIn 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
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.
Повний текст джерелаMohammadisohrabi, Ali. "Design and implementation of a Recurrent Neural Network for Remaining Useful Life prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Знайти повний текст джерелаNguyen, Hoang-Phuong. "Model-based and data-driven prediction methods for prognostics." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASC021.
Повний текст джерелаDegradation 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
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.
Повний текст джерелаThe 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
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.
Повний текст джерелаThe 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
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.
Повний текст джерелаToday, 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
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.
Повний текст джерелаAdditiv 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.
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.
Повний текст джерелаShaping 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
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.
Повний текст джерелаThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
Full Text
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.
Повний текст джерелаIncludes 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.
Tyagi, Prakhar. "Chassis predictive maintenance and service solutions." Thesis, KTH, Fordonsdynamik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265587.
Повний текст джерелаPrediktivt 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.
Korvesis, Panagiotis. "Machine Learning for Predictive Maintenance in Aviation." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX093/document.
Повний текст джерелаThe 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
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.
Повний текст джерелаUunk, 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.
Повний текст джерелаKarlsson, 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.
Повний текст джерелаSedghi, 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.
Повний текст джерелаKilleen, Patrick. "Knowledge-Based Predictive Maintenance for Fleet Management." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40086.
Повний текст джерелаAguilar, 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/.
Повний текст джерелаIntensive 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).
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.
Повний текст джерелаElförsörjningen 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.
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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.
Повний текст джерелаEl 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.
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.
Повний текст джерелаDe, 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/.
Повний текст джерелаFURTADO, 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.
Повний текст джерелаCom 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.
Gorman, Joe, Glenn Takata, Subhash Patel, and Dan Grecu. "A Constraint-Based Approach to Predictive Maintenance Model Development." International Foundation for Telemetering, 2008. http://hdl.handle.net/10150/606187.
Повний текст джерелаPredictive 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.