This paper presents a new adaptive sampling approach based on a novel integrated performance measure approach, referred to as “iPMA,” for system reliability assessment with multiple dependent failure events. The developed approach employs Gaussian process (GP) regression to construct surrogate models for each component failure event, thereby enables system reliability estimations directly using Monte Carlo simulation (MCS) based on surrogate models. To adaptively improve the accuracy of the surrogate models for approximating system reliability, an iPM, which envelopes all component level failure events, is developed to identify the most useful sample points iteratively. The developed iPM possesses three important properties. First, it represents exact system level joint failure events. Second, the iPM is mathematically a smooth function “almost everywhere.” Third, weights used to reflect the importance of multiple component failure modes can be adaptively learned in the iPM. With the weights updating process, priorities can be adaptively placed on critical failure events during the updating process of surrogate models. Based on the developed iPM with these three properties, the maximum confidence enhancement (MCE) based sequential sampling rule can be adopted to identify the most useful sample points and improve the accuracy of surrogate models iteratively for system reliability approximation. Two case studies are used to demonstrate the effectiveness of system reliability assessment using the developed iPMA methodology.
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February 2015
Research-Article
An Integrated Performance Measure Approach for System Reliability Analysis
Zequn Wang,
Zequn Wang
Department of Industrial and
Manufacturing Engineering,
e-mail: zxwang5@wichita.edu
Manufacturing Engineering,
Wichita State University
,Wichita, KS 67260
e-mail: zxwang5@wichita.edu
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Pingfeng Wang
Pingfeng Wang
1
Assistant Professor
Department of Industrial and
Manufacturing Engineering,
e-mail: pingfeng.wang@wichita.edu
Department of Industrial and
Manufacturing Engineering,
Wichita State University
,Wichita, KS 67260
e-mail: pingfeng.wang@wichita.edu
1Corresponding author.
Search for other works by this author on:
Zequn Wang
Department of Industrial and
Manufacturing Engineering,
e-mail: zxwang5@wichita.edu
Manufacturing Engineering,
Wichita State University
,Wichita, KS 67260
e-mail: zxwang5@wichita.edu
Pingfeng Wang
Assistant Professor
Department of Industrial and
Manufacturing Engineering,
e-mail: pingfeng.wang@wichita.edu
Department of Industrial and
Manufacturing Engineering,
Wichita State University
,Wichita, KS 67260
e-mail: pingfeng.wang@wichita.edu
1Corresponding author.
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 22, 2014; final manuscript received November 15, 2014; published online December 15, 2014. Assoc. Editor: Irem Y. Tumer.
J. Mech. Des. Feb 2015, 137(2): 021406 (11 pages)
Published Online: February 1, 2015
Article history
Received:
March 22, 2014
Revision Received:
November 15, 2014
Online:
December 15, 2014
Citation
Wang, Z., and Wang, P. (February 1, 2015). "An Integrated Performance Measure Approach for System Reliability Analysis." ASME. J. Mech. Des. February 2015; 137(2): 021406. https://doi.org/10.1115/1.4029222
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