An artificial neural network approach for remaining useful life prediction of equipments 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. An artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equip...

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Bibliographic Details
Published in2009 8th International Conference on Reliability, Maintainability and Safety pp. 143 - 148
Main Author Zhigang Tian
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
accurate
artificial neural network
Artificial neural networks
Autoregressive processes
bearing
Condition monitoring
Degradation
Inspection
Maintenance
Neural networks
prediction
Prediction methods
Predictive models
remaining useful life
Vibrations
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Summary:Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. 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. Techniques are introduced to reduce the effects of the noise factors that are irrelevant to equipment degradation. The proposed method is validated using real-world vibration monitoring data.
ISBN:1424449030
9781424449033
DOI:10.1109/ICRMS.2009.5270220