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Добірка наукової літератури з теми "MAINTENANCE PREDICTION"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "MAINTENANCE PREDICTION"

Marshall, David F. "Language Maintenance and Revival." Annual Review of Applied Linguistics 14 (March 1994): 20–33. http://dx.doi.org/10.1017/s0267190500002798.

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Johann Gottfreid Herder illustrated how problematic language maintenance predictions can be with his prediction in his essay, On the Origin of Languages, stating that Hungarian would briefly disappear from the surface of the earth as if it had never existed. With over 10 million speakers today in Hungary, another 4 million outside the nation, and a growing population (Hungarians in the Outside World 1993), Herder's prediction remains hyperbolic, yet it illustrates how dangerous such predictions about language maintenance can be. Hungarian, as with other languages, has been maintained because of forces operating in the unique history of the nation.

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Xu, Peng, Rengkui Liu, Quanxin Sun, and Futian Wang. "A Novel Short-Range Prediction Model for Railway Track Irregularity." Discrete Dynamics in Nature and Society 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/591490.

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In recent years, with axle loads, train loads, transport volume, and travel speed constantly increasing and railway network steadily lengthening, shortcomings of current maintenance strategies are getting to be noticed from an economical and safety perspective. To overcome the shortcomings, permanent-of-way departments throughout the world have given a considerable attention to an ideal maintenance strategy which is to carry out appropriate maintenances just in time on track locations really requiring maintenance. This strategy is simplified as the condition-based maintenance (CBM) which has attracted attentions of engineers of many industries in the recent 70 years. To implement CBM for track irregularity, there are many issues which need to be addressed. One of them focuses on predicting track irregularity of each day in a future short period. In this paper, based on track irregularity evolution characteristics, a Short-Range Prediction Model was developed to this aim and is abbreviated to TI-SRPM. Performance analysis results for TI-SRPM illustrate that track irregularity amplitude predictions on sampling points by TI-SRPM are very close to their measurements by Track Geometry Car.

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Nansamba, Salmah, and Hadi Harb. "Developing a Neural Network Based Fault Prediction Tool for a Solar Power Plant in Uganda." Transactions on Machine Learning and Artificial Intelligence 10, no. 6 (December 28, 2022): 52–70. http://dx.doi.org/10.14738/tmlai.106.13645.

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Solar photovoltaic (PV) systems are one of the fastest growing renewable energy technologies and plenty of research has been and continues to be carried out in this domain. Maximization of solar PV power plant production, efficiency and return on investment can only be achieved by having adequate and effective maintenance systems in place. Of the various maintenance schemes, predictive maintenance is popular for its effectiveness and minimization of resource wastage. Maintenance activities are scheduled based on the real time condition of the system with priority being given to the system components with the highest likelihood of failure. A good predictive maintenance system is based on the premise of being able to anticipate faults before they occur. In this study therefore, a fault prediction tool for a solar plant in Uganda is proposed. The hybrid tool is developed using both feed forward and long short term memory neural networks for power prediction, in conjunction with a mean chart statistical process control tool for final fault prediction. Results from the study demonstrate that the feed forward and long short term memory neural network modules of the proposed tool attain mean absolute errors of 4.2% and 6.9% respectively for power production predictions. The fault prediction capability of the tool is tested under both normal and abnormal operating conditions. Results show that the tool satisfactorily discriminates against the fault and non-fault conditions thereby achieving successful solar PV system fault prediction.

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Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks." Sensors 21, no. 3 (January 30, 2021): 932. http://dx.doi.org/10.3390/s21030932.

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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

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Fitra Azyus, Adryan, Sastra Kusuma Wijaya, and Mohd Naved. "Determining RUL Predictive Maintenance on Aircraft Engines Using GRU." Journal of Mechanical, Civil and Industrial Engineering 3, no. 3 (December 11, 2022): 79–84. http://dx.doi.org/10.32996/jmcie.2022.3.3.10.

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Prognostic and health management (PHM) in the aviation industry is expanding because of its effect on economic and human safety. Advanced maintenance shall be applied to this industry to inform aircraft engine conditions. PdM (Predictive Maintenance) is an advanced maintenance technique that can be applied to the aviation industry because of its high-precision prediction. Combining PdM as a technique to calculate the RUL (Remaining Useful Lifetime ) and ML (Machine Learning) as a tool to make high-accuracy predictions is mixed together that accurately forecasts the state of aircraft machine condition and on the best time to get the maintenance or service. In this work, we use the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set. This work proposes GRU to determine RUL on aircraft engines to implement a Predictive maintenance strategy. For the training parameters tested in this study, we used a batch size of 512, a learning rate with Adam optimizer of 0.001, then epochs of 200. The essence of the results of this experiment is to obtain a new method with a simpler calculation process and the epoch value and a faster prediction process compared to other methods used, and the results obtained can approach the original value from an economic point of view and the RUL prediction process using the GRU.

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D., Ganga, and Ramachandran V. "Adaptive prediction model for effective electrical machine maintenance." Journal of Quality in Maintenance Engineering 26, no. 1 (April 18, 2019): 166–80. http://dx.doi.org/10.1108/jqme-12-2017-0087.

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Purpose The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy. Design/methodology/approach The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA) time series prediction. ARMA prediction has been carried out using different combinations of AR and MA methods in order to make prediction with less Mean Squared Error (MSE). Findings The prediction error indicates the inadequacy of the model to estimate the data characteristics, which has been resolved at the subsequent stage by cascading an adaptive least mean square finite impulse response filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence. Research limitations/implications The componentized data prediction based on time series and cascade adaptive filter algorithm decomposes the non-stationary data characteristics for predictive maintenance. Evaluation of the model with different combination of time series algorithms and parameter settings of adaptive filter has been carried out to illustrate the performance of the prediction model. This prediction accuracy is compared with existing linear adaptive filter prediction using MSE as comparison index. The wide margin in the MSE values substantiates the prediction efficiency of the proposed model for machine data. Originality/value This model predicts the dynamic machine data with component decomposition at high accuracy, which enables to interpret the system response under dynamic conditions efficiently.

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Tong, Guoqiang, Xinbo Qian, and Yilai Liu. "Prognostics and Predictive Maintenance Optimization Based on Combination BP-RBF-GRNN Neural Network Model and Proportional Hazard Model." Journal of Sensors 2022 (April 29, 2022): 1–17. http://dx.doi.org/10.1155/2022/8655669.

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Owning to the advantage of keeping the operating environment safe, high reliability, and low production cost, predictive maintenance has been widely used in industry and academia. Predictive maintenance based on degeneration state mainly studies the degeneration prediction. However, on account of the error of the sensor and human, condition monitoring data may not directly reflect the true degeneration. The degeneration model with dynamic explanatory covariates which is named as proportional hazard model is proposed to deal with the semi-observed monitoring condition. And the degeneration prediction mainly adopts a single prediction model, which leads to low prediction accuracy. A combination forecasting model can effectively solve the above problem. Compared to the traditional prediction method, the neural network model can use the “black box” characteristic to indirectly construct the degeneration model without complex mathematical derivation. Therefore, we propose a combination BP-RBF-GRNN neural network model which is applied to improve the degeneration prediction with dynamic covariate. Based on the above two aspects, a predictive maintenance optimization framework based on the proportional hazard model and BP-RBF-GRNN neural network model is proposed to improve maintenance efficiency and reduce maintenance costs. The simulation results of thrust ball bearing show that the proposed method can effectively improve the degeneration prediction accuracy and reduce the maintenance cost rate to a certain extent.

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Rodrigues, Joao, Jose Torres Farinha, and Antonio Marques Cardoso. "Predictive Maintenance Tools – A Global Survey." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (January 22, 2021): 96–109. http://dx.doi.org/10.37394/23203.2021.16.7.

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The importance given to the maintenance in the industrial world has grown over time, with new methods, new procedures and new challenges, due to the availability of new technologies. This paper focus on a global survey about predictive maintenance tools that support predictive maintenance, from the time series and decision trees until Artificial Intelligence. The approach of the several tools that can help the prediction is holistic, because new tools do not eliminate the importance of the old ones: they are complimentary and each new tool that is developed add potential for a better prediction. Additionally, it must be emphasized that some tools, that seem new are, in practice, old tools with new and powerful computational devices, assuming a new and strategic importance nowadays.

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Gibiec, Mariusz. "Prediction of Machines Health with Application of an Intelligent Approach – a Mining Machinery Case Study." Key Engineering Materials 293-294 (September 2005): 661–68. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.661.

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Machine field services depend on sensor-driven management systems that provide alerts, alarms and indicators. At the moment the alarm is sounded, it’s sometimes too late to prevent the failure. There is no alert provided that looks at degradation over time. If we could monitor degradation, then we would forecast upcoming situations, and perform maintenance tasks when necessary. In our research we chose to focus on intelligent maintenance system, which is defined as the prediction and forecast of equipment performance. Predictive maintenance, on the other hand, focuses on machine performance features. Data come from two sources: sensors mounted on the machine to gather the machine feature information, and information from the entire manufacturing system, including machine productivity, past history and trending. By correlating data from these sources — current and historical — predictions can be made about future performance. In this article case study of coal mining machinery health prediction is presented. Health of water pumping unit was considered. Such units placed in old mine shafts are crucial to avoid flooding working ones. As an effect of predictive maintenance it can be possible to improve safety and reduce costs incurred from accidents.

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Zhu, Ya Hong, Ji Ping Cao, Wen Xia Sun, Yang Tao Fan, and Zhi Hui Zhao. "Demand Forecasting Model Based on Equipment Maintenance Resources in Virtual Warehousing." Applied Mechanics and Materials 556-562 (May 2014): 5442–49. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5442.

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Based on the theory of virtual warehousing, the optimization system for equipment maintenance resources in virtual warehousing is established for the security task of equipment maintenance resources. According to the prediction problems on the spare parts requirements for equipment maintenance in this system, the demand forecasting model, based on the combination of rough sets and grey prediction, is adopted. The results of simulation experiment show that this method applied in equipment maintenance spare resources prediction is reliable and with accurate information. While, the relative error and absolute error of the predictive value and practical value are very small, which shows the prediction model is of high precision for the accurate effect prediction. As a result, this model and algorithum is proved to be effective to provide theoretical and practical support for equipment maintenance spare resources in information warfare.

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Більше джерел

Дисертації з теми "MAINTENANCE PREDICTION"

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.

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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 proposées
In 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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Книги з теми "MAINTENANCE PREDICTION"

Foundation, AWWA Research, and American Water Works Association, eds. Main break prediction, prevention, and control. Denver, Colo: Awwa Research Foundation, 2007.

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Taynor, Janet. Prediction model for estimating performance impacts of maintenance stress. Brooks Air Force Base, Tex: Air Force Systems Command, Air Force Human Resources Laboratory, 1988.

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Hu, Changhua, Hongdong Fan, and Zhaoqiang Wang. Residual Life Prediction and Optimal Maintenance Decision for a Piece of Equipment. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-2267-0.

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Liebermann, R. C. Stony Brook seismic network on Long Island, New York: Operation and maintenance, final report September 1979 - March 1985. Washington, D.C: Division of Radiation Programs and Earth Sciences, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1986.

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Gregory, Williamson, Weyers Richard E, Brown Michael Carey 1969-, Sprinkel Michael M, Virginia Transportation Research Council, and Virginia. Dept. of Transportation., eds. Bridge deck service life prediction and costs. Charlottesville, Va: Virginia Transportation Research Council, 2007.

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International RILEM Workshop on Life Prediction and Aging Management of Concrete Structures (2003 Paris, France). 2nd International RILEM Workshop on Life Prediction and Aging Management of Concrete Structures : Paris, France, 5-6 May 2003. Bagneux: RILEM Publications, 2003.

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Bartels, Bjoern. Strategies to the prediction, mitigation and management of product obsolescence. Hoboken, NJ: Wiley, 2012.

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Youakim, Samer Amir. A simplified method for prediction of long-term prestress loss in post-tensioned concrete bridges. La Jolla, Calif: Dept. of Structural Engineering, University of California, San Diego, 2006.

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Pecht, Michael. Life-cycle forecasting, mitigation assessment, and obsolescence strategies: A guide to the prediction and management of electronic parts obsolescence. College Park, Md: CALCE EPSC Press, 2002.

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An introduction to predictive maintenance. New York, NY: Van Nostrand Reinhold, 1990.

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Частини книг з теми "MAINTENANCE PREDICTION"

Torim, Ants, Innar Liiv, Chahinez Ounoughi, and Sadok Ben Yahia. "Pattern Based Software Architecture for Predictive Maintenance." In Communications in Computer and Information Science, 26–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_3.

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AbstractMany industrial sectors are moving toward Industry Revolution (IR) 4.0. In this respect, the Internet of Things and predictive maintenance are considered the key pillars of IR 4.0. Predictive maintenance is one of the hottest trends in manufacturing where maintenance work occurs according to continuous monitoring using a healthiness check for processing equipment or instrumentation. It enables the maintenance team to have an advanced prediction of failures and allows the team to undertake timely corrective actions and decisions ahead of time. The aim of this paper is to present a smart monitoring and diagnostics system as an expert system that can alert an operator before equipment failures to prevent material and environmental damages. The main novelty and contribution of this paper is a flexible architecture of the predictive maintenance system, based on software patterns - flexible solutions to general problems. The presented conceptual model enables the integration of an expert knowledge of anticipated failures and the matrix-profile technique based anomaly detection. The results so far are encouraging.

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Pohlkötter, Fabian J., Dominik Straubinger, Alexander M. Kuhn, Christian Imgrund, and William Tekouo. "Unlocking the Potential of Digital Twins." In Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains, 190–99. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27933-1_18.

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AbstractIncreasing competitive pressure is confronting the automotive industry with major challenges. As a result, conventional reactive maintenance is being transformed into predictive maintenance. In this context, wearing and aging effects no longer lead to plant failure since they are predicted at an earlier stage based on comprehensive data analysis.Furthermore, the evolution towards Smart Factory has given rise to virtual commissioning in the planning phase of production plants. In this process, a Hardware-in-the-Loop (HiL) system combines the real controls (e.g., PLC) and a virtual model of the plant. These HiL systems are used to simulate commissioning activities in advance, thus saving time and money during actual commissioning. The resulting complex virtual models are not further used in the series production.This paper builds upon virtual commissioning models to develop a Digital Twin, which provides inputs for predictive maintenance. The resulting approach is a methodology for building a hybrid predictive maintenance system. A hybrid prediction model combines the advantages of data-driven and physical models. Data-driven models analyse and predict wearing patterns based on real machine data. Physical models are used to reproduce the behaviour of a system. From the simulation of the hybrid model, additional insights for the predictions can be derived.The conceptual methodology for a hybrid predictive maintenance system is validated by the successful implementation in a bottleneck process of the electric engine production for an automotive manufacturer. Ultimately, an outlook on further possible applications of the hybrid model is presented.

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Orchard, Marcos E., and David E. Acuña. "On Prognostic Algorithm Design and Fundamental Precision Limits in Long-Term Prediction." In Predictive Maintenance in Dynamic Systems, 355–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_12.

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Gómez Fernández, Juan Francisco, Jesús Ferrero Bermejo, Fernando Agustín Olivencia Polo, Adolfo Crespo Márquez, and Gonzalo Cerruela García. "Dynamic Reliability Prediction of Asset Failure Modes." In Advanced Maintenance Modelling for Asset Management, 291–309. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58045-6_12.

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Lughofer, Edwin, Alexandru-Ciprian Zavoianu, Mahardhika Pratama, and Thomas Radauer. "Automated Process Optimization in Manufacturing Systems Based on Static and Dynamic Prediction Models." In Predictive Maintenance in Dynamic Systems, 485–531. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_17.

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Wu, Peggy, Jacquelyn Morie, J. Benton, Kip Haynes, Eric Chance, Tammy Ott, and Sonja Schmer-Galunder. "Social Maintenance and Psychological Support Using Virtual Worlds." In Social Computing, Behavioral-Cultural Modeling and Prediction, 393–402. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05579-4_48.

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Anderson, Ronald T., and Lewis Neri. "The Army Aircraft Flight Safety Prediction Model." In Reliability-Centered Maintenance: Management and Engineering Methods, 275–311. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0757-7_6.

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De Lucia, Andrea, Eugenio Pompella, and Silvio Stefanucci. "Assessing Effort Prediction Models for Corrective Software Maintenance." In Enterprise Information Systems VI, 55–62. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-3675-2_7.

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Bharathi, V., and Udaya Shastry. "Neural Network Based Effort Prediction Model for Maintenance Projects." In Communications in Computer and Information Science, 236–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21233-8_29.

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Zeng, Yi, Wei Jiang, Changan Zhu, Jianfeng Liu, Weibing Teng, and Yidong Zhang. "Prediction of Equipment Maintenance Using Optimized Support Vector Machine." In Lecture Notes in Computer Science, 570–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_69.

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Тези доповідей конференцій з теми "MAINTENANCE PREDICTION"

Mishra, KamalaKanta, and Sachin Kumar Manjhi. "Failure Prediction Model for Predictive Maintenance." In 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2018. http://dx.doi.org/10.1109/ccem.2018.00019.

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Zhou, J., X. Li, A. J. R. Andernroomer, H. Zeng, K. M. Goh, Y. S. Wong, and G. S. Hong. "Intelligent prediction monitoring system for predictive maintenance in manufacturing." In 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005. IEEE, 2005. http://dx.doi.org/10.1109/iecon.2005.1569264.

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Hafeez, Abdul Basit, Eduardo Alonso, and Aram Ter-Sarkisov. "Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance." In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00167.

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Bundasak, Supaporn, and Pawin Wittayasirikul. "Predictive maintenance using AI for Motor health prediction system." In 2022 International Electrical Engineering Congress (iEECON). IEEE, 2022. http://dx.doi.org/10.1109/ieecon53204.2022.9741620.

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Su, Xiaobo, Qi Gao, Qingchun Wu, and Jingxiong Gao. "Preventive Maintenance Task Prediction Based on Hierarchical Maintenance Conversion Law." In 2020 Prognostics and Health Management Conference (PHM-Besançon). IEEE, 2020. http://dx.doi.org/10.1109/phm-besancon49106.2020.00054.

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Mosallam, Ahmed, Stefan Byttner, Magnus Svensson, and Thorsteinn Rognvaldsson. "Nonlinear Relation Mining for Maintenance Prediction." In 2011 IEEE Aerospace Conference. IEEE, 2011. http://dx.doi.org/10.1109/aero.2011.5747581.

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Korvesis, Panagiotis, Stephane Besseau, and Michalis Vazirgiannis. "Predictive Maintenance in Aviation: Failure Prediction from Post-Flight Reports." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00160.

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Yuguo Xu, Yaohui Zhang, and Shixin Zhang. "Uncertain generalized remaining useful life prediction-driven predictive maintenance decision." In 2015 Prognostics and System Health Management Conference (PHM). IEEE, 2015. http://dx.doi.org/10.1109/phm.2015.7380097.

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Olariu, Eliza Maria, Raluca Portase, Ramona Tolas, and Rodica Potolea. "Predictive Maintenance - Exploring strategies for Remaining Useful Life (RUL) prediction." In 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2022. http://dx.doi.org/10.1109/iccp56966.2022.10053988.

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van Driel, W. D., J. G. J. Beijer, J. W. Bikker, C. H. M. van Blokland, C. Ankomah, and B. Jacobs. "Color maintenance prediction for LED-based products." In 2018 19th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE). IEEE, 2018. http://dx.doi.org/10.1109/eurosime.2018.8369875.

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Звіти організацій з теми "MAINTENANCE PREDICTION"

Ritchie, R. J., J. C. Notestine, J. S. Schmidt, J. N. Irvin, and C. P. Vaziri. Prediction of Scheduled and Preventative Maintenance Workload. Fort Belvoir, VA: Defense Technical Information Center, January 1985. http://dx.doi.org/10.21236/ada153761.

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Bubenik, T. A., R. D. Fischer, G. R. Whitacre, D. J. Jones, J. F. Kiefner, M. Cola, and W. A. Bruce. API-WCR Investigation and Prediction of Cooling Rates During Pipeline Maintenance Welding. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 1991. http://dx.doi.org/10.55274/r0011852.

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Investigates and improves methods of predicting cooling rates during pipeline maintenance welding. This project was funded by the American Petroleum Institute. The work was performed by Battelle Memorial Institute and Edison Welding Institute. The scope of work included (1) a review of three previous research efforts to develop satisfactory methods for welding appurtenances to in-service pipelines, (2) a review of a pipeline leak and rupture incidents associated with appurtenances, (3) the enhancement of existing analytical models for predicting cooling rates and temperatures during welding on an in-service pipeline, and (4) validation of the thermal-analysis models by performing welds on pipelines carrying three different liquid-petroleum products. The thermal-analysis models can be used to help develop maintenance welding procedures for repair and hot tap welding applications and to reassess the condition of existing installations. This work was cofounded by PRC.

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Leis. L51866 Field Studies to Support SCC Life Prediction Model. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 1997. http://dx.doi.org/10.55274/r0010357.

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One objective of this project was to gather and analyze SCC field data on lines being retested for use in assessing the validity of current or future SCC models. The scope of this initial study was limited to colonies of SCC in one valve section of a pipeline that runs from Texas to the northeast of the United States. This valve section had an early history of high pH SCC. The susceptibility since has been controlled through hydrotesting and modifications to the gas compression to meet upstream demand while reducing the discharge temperature. In addition to collecting data to validate models of SCC, data were also developed to evaluate the suitability of a hand-held tool to measure the depth of SCC, because such results can be critical in the use of models in making serviceability and maintenance decisions.

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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.

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Cheng and Wang. L52025 Calibration of the PRCI Thermal Analysis Model for Hot Tap Welding. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 2004. http://dx.doi.org/10.55274/r0010298.

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In-service welding is a common industrial practice for both maintenance and repair purpose. Its applications include, but not limited to repair of pipeline damages caused by construction or corrosion, and hot tap welding used to add branch connections to existing pipelines. In-service welding enables maintaining and repairing pipelines without removing them from service. Such welding operations generate significant economic and environmental benefits, for example, no interruption of pipeline operations and no venting of pipeline contents. One of the common problems associated with in-service welding is hydrogen cracking. Pipeline operating conditions combined with unscrupulous welding procedures could lead to high heat-affected zone (HAZ) hardness values and this, in turn, could cause hydrogen cracking. The risk of hydrogen cracking is particularly high for older pipeline materials with high carbon equivalent. The objective of the project was to produce a significantly improved HAZ hardness prediction procedure over the procedure in the current PRCI thermal analysis software by utilizing state-of-the-art phase transformation models for steels. Systematic validation of the prediction algorithms was conducted using extensive experimental data of actual welds. The hardness prediction model is expected to become the basis on which the hardness prediction module of the PRCI thermal analysis software will be upgraded and improved.

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Church, Joshua, LaKenya Walker, and Amy Bednar. JAIC Predictive Maintenance Dashboard user manual. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41823.

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This manual is intended for new users with minimal or no experience with using the JAIC Predictive Maintenance Dashboard (JPD). The goal of this document is to give an overview of the main functions of JPD. The primary focus of this document is to demonstrate functionality. Every effort has been made to ensure this document is an accurate representation of the functionality of the JPD. For additional information about this manual, contact ERDC.JAIC@erdc.dren.mil.

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Been. L52121 Coating Deterioration as a Precursor for SCC. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2004. http://dx.doi.org/10.55274/r0011093.

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The selection and prioritization of field locations that most warrant direct examination of the pipe or other maintenance activities requires prediction of the coating conditions and the environmental conditions underneath a disbonded coating, which may support corrosion or cracking.� Current above ground measurements provide little information and to improve our ability of site-selection, this project considered a combined experimental and modeling approach to specifically identify those coating properties and environmental conditions that can lead to damaging SCC environments. Soil box experiments indicated that near-neutral pH SCC environments are supported by shielding coatings and intermediate conductivity soils, where increased pH levels can be the result of improved current penetration in high conductivity soils or low buffering capacity in low conductivity soils.� As the pH increases, the environment becomes less conducive to near-neutral pH SCC.� TECTRAN modeling indicated an important role of coating permeability to CO2 in maintaining a near-neutral pH.� Degradation of the mastic adhesive may be another source of CO2.

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Klein, Gary A., Sallie E. Gordon, Mark Palmisano, and Angelo Mirabella. Comparison-Based Predictions and Recommendations for Army Maintenance Training Devices. Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada170942.

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Unknown, Author. WINMOP-R03 Performance of Offshore Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2003. http://dx.doi.org/10.55274/r0011744.

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The objective of the project was to validate existing pipeline integrity prediction models through field testing multiple pipelines, validate the performance of in-line instrumentation through smart pig runs, and finally, to assess the actual integrity of aging damaged and defective pipelines. The objectives were accomplished by the testing of aging out-of-service lines using "smart pigs", followed by hydrotesting of the lines to failure, recovery of the failed sections, and determination of the pipeline characteristics in the vicinity of the failed sections (failure analysis). One objective of the project was to validate the dented, gouged, and corroded pipeline burst strength prediction models currently in existence, such as ASME B31-G, R-Streng, and DNV 99 for pipelines. Another model was being developed as a joint international project sponsored by the U. S. Minerals Management Service, Petroleos Mexicanos (PEMEX), and Instituto Mexicano del Petroleo (IMP) titled RAM PIPE REQUAL and an associated JIP identified as PIMPIS (Pipeline Inspection, Maintenance, and Performance Information System), this would be tested and validated as well. The validation was provided by hydrotesting in-situ pipelines to failure. Sustained and rapidly applied hydro-pressures were used to investigate the effects of delayed and dynamic pressure related failures. After testing, the pipelines were scheduled for decommissioning; with the failed sections located, and brought to the laboratory for testing and analysis. Class A predictions were made before the pipelines were hydrotested to failure based on results from in-line instrumentation (instrumented) and from knowledge of the pipeline products and other characteristics (not instrumented). Based on the results from the testing, the analytical models were to be revised to provide improved agreement between the measured and predicted burst pressures. Since the pipelines were inspected with smart pigs before the hydro-tests, it was possible to compare the smart-pig data gathered during pig runs to the actual condition of the pipeline. This was accomplished by recovering sections of the pipeline that were identified by the pig as having pits or metal-loss areas. Reviewed pipeline decommissioning inventory and selected a pipeline candidate. The specific scope of work included: � Selected pipelines for testing. � Conducted field tests with an instrumented pig to determine pipeline denting, gouging and corrosion conditions. � Used existing analytical models to determine burst strength for both instrumented and non-instrumented pipelines. � Hydrotested the selected pipelines to failure (sustained and rapidly applied pressures). � Located and retrieve failed sections and other sections identified as problem spots by the "smart-pig." � Compared "smart pig" data to actual pipeline condition. � Analyzed the failed sections to determine their physical and material characteristics. � Revised the analytical models to provide improved agreements between predicted and measured burst pressures.

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Foster, Michelle. Vibration Analysis - Presented to the MMWG Predictive Maintenance User’s Group. Office of Scientific and Technical Information (OSTI), August 2023. http://dx.doi.org/10.2172/1996132.

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