Remaining useful life prediction of individual units subject to hard failure

Qiang Zhou; Junbo Son; Shiyu Zhou; Xiaofeng Mao; Mutasim Salman

doi:10.1080/0740817X.2013.876126

Remaining useful life prediction of individual units subject to hard failure

Qiang Zhou
, Junbo Son
, Shiyu Zhou
, Xiaofeng Mao
, Mutasim Salman

Research output: Contribution to journal › Article › peer-review

92 Scopus citations

Abstract

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. © 2014

Original language	English (US)
Pages (from-to)	1017-1030
Number of pages	14
Journal	IIE Transactions (Institute of Industrial Engineers)
Volume	46
Issue number	10
DOIs	https://doi.org/10.1080/0740817X.2013.876126
State	Published - Oct 3 2014
Externally published	Yes

Keywords

Remaining useful life prediction
hard failure
joint model

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

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10.1080/0740817X.2013.876126

Cite this

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title = "Remaining useful life prediction of individual units subject to hard failure",

abstract = "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. {\textcopyright} 2014",

keywords = "Remaining useful life prediction, hard failure, joint model",

author = "Qiang Zhou and Junbo Son and Shiyu Zhou and Xiaofeng Mao and Mutasim Salman",

note = "Funding Information: This work is supported by the National Science Foundation under grant 1335129 and the National Natural Science Foundation of China under grant 11301441. Funding Information: Shiyu Zhou is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin–Madison. He received his B.S. and M.S. in Mechanical Engineering from the University of Science and Technology of China in 1993 and 1996, respectively, and his master{\textquoteright}s in Industrial Engineering and Ph.D. in Mechanical Engineering from the University of Michigan in 2000. His research interests include in-process quality and productivity improvement methodologies by integrating statistics, system and control theory, and engineering knowledge. His research is sponsored by the National Science Foundation, Department of Energy, Department of Commerce, and industry. He is a recipient of a CAREER Award from the National Science Foundation and the Best Application Paper Award from IIE Transactions. He is a member of IIE, INFORMS, ASME, and SME.",

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doi = "10.1080/0740817X.2013.876126",

language = "English (US)",

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AU - Zhou, Qiang

AU - Son, Junbo

AU - Zhou, Shiyu

AU - Mao, Xiaofeng

AU - Salman, Mutasim

N1 - Funding Information: This work is supported by the National Science Foundation under grant 1335129 and the National Natural Science Foundation of China under grant 11301441. Funding Information: Shiyu Zhou is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin–Madison. He received his B.S. and M.S. in Mechanical Engineering from the University of Science and Technology of China in 1993 and 1996, respectively, and his master’s in Industrial Engineering and Ph.D. in Mechanical Engineering from the University of Michigan in 2000. His research interests include in-process quality and productivity improvement methodologies by integrating statistics, system and control theory, and engineering knowledge. His research is sponsored by the National Science Foundation, Department of Energy, Department of Commerce, and industry. He is a recipient of a CAREER Award from the National Science Foundation and the Best Application Paper Award from IIE Transactions. He is a member of IIE, INFORMS, ASME, and SME.

PY - 2014/10/3

Y1 - 2014/10/3

N2 - 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. © 2014

AB - 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. © 2014

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KW - hard failure

KW - joint model

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Remaining useful life prediction of individual units subject to hard failure

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Keywords

ASJC Scopus subject areas

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