Remaining useful life prediction of individual units subject to hard failure

To develop a cost-effective condition-based maintenance strategy, accurate prediction of the Remaining Useful Life (RUL) is the key. It is known that many failure mechanisms in engineering can be traced back to some underlying degradation processes. This article proposes a two-stage prognostic frame...

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Bibliographic Details
Published inIIE transactions Vol. 46; no. 10; pp. 1017 - 1030
Main Authors Zhou, Qiang, Son, Junbo, Zhou, Shiyu, Mao, Xiaofeng, Salman, Mutasim
Format Journal Article
LanguageEnglish
Published Norcross Taylor & Francis 03.10.2014
Taylor & Francis Ltd
Subjects
Algorithms
Automotive components
Battery
Degradation
Effectiveness studies
Failure
Failure analysis
hard failure
joint model
Life prediction
Loads (forces)
Mathematical models
Predictions
Remaining useful life prediction
Simulation
Strategy
Useful life
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Summary:To develop a cost-effective condition-based maintenance strategy, accurate prediction of the Remaining Useful Life (RUL) is the key. It is known that many failure mechanisms in engineering can be traced back to some underlying degradation processes. This article proposes a two-stage prognostic framework for individual units subject to hard failure, based on joint modeling of degradation signals and time-to-event data. The proposed algorithm features a low computational load, online prediction, and dynamic updating. Its application to automotive battery RUL prediction is discussed in this article as an example. The effectiveness of the proposed method is demonstrated through a simulation study and real data.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0740-817X
2472-5854
1545-8830
2472-5862
DOI:10.1080/0740817X.2013.876126