An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life pred...

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Veröffentlicht in:	Journal of intelligent manufacturing Jg. 23; H. 2; S. 227 - 237
1. Verfasser:	Tian, Zhigang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.04.2012 Springer Nature B.V
Schlagworte:	Accuracy Age Analysis Bearings Business and Management Case studies Comparative studies Control Crack initiation Failure Intelligent systems Machines Maintenance costs Maintenance management Manufacturing Mechatronics Methods Monitoring Neural networks Physics Processes Production Robotics Studies Useful life Artificial neural network Remaining useful life Prediction Accurate Bearing
ISSN:	0956-5515, 1572-8145
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Abstract	Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.
AbstractList	Issue Title: Special Issues: "Machinery Health Monitoring, Diagnosis and Prognosis" and "Condition-Based Maintenance: Theory and Applications" Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.[PUBLICATION ABSTRACT] Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.
Author	Tian, Zhigang
Author_xml	– sequence: 1 givenname: Zhigang surname: Tian fullname: Tian, Zhigang email: tian@ciise.concordia.ca organization: Concordia Institute for Information Systems Engineering, Concordia University
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ContentType	Journal Article
Copyright	Springer Science+Business Media, LLC 2009 Springer Science+Business Media, LLC 2012
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Keywords	Artificial neural network Remaining useful life Prediction Accurate Bearing
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Snippet	Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall... Issue Title: Special Issues: "Machinery Health Monitoring, Diagnosis and Prognosis" and "Condition-Based Maintenance: Theory and Applications" Accurate...
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SubjectTerms	Accuracy Age Analysis Bearings Business and Management Case studies Comparative studies Control Crack initiation Failure Intelligent systems Machines Maintenance costs Maintenance management Manufacturing Mechatronics Methods Monitoring Neural networks Physics Processes Production Robotics Studies Useful life
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