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Research Papers: Design Automation

An Integrated Performance Measure Approach for System Reliability Analysis

[+] Author and Article Information
Zequn Wang

Department of Industrial and
Manufacturing Engineering,
Wichita State University,
Wichita, KS 67260
e-mail: zxwang5@wichita.edu

Pingfeng Wang

Assistant Professor
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 137(2), 021406 (Feb 01, 2015) (11 pages) Paper No: MD-14-1187; doi: 10.1115/1.4029222 History: Received March 22, 2014; Revised November 15, 2014; Online December 15, 2014

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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Figures

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Fig. 1

Concept of system reliability analysis (two performance functions)

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Fig. 2

Smoothness of iPM function

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Fig. 3

Three component performance functions

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Fig. 5

Flow chart of system reliability assessment using iPMA

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Fig. 6

Estimated integrated limit state using GP regression for first system input

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Fig. 7

Estimated integrated limit state using GP regression for second system input

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Fig. 8

Estimated integrated limit state using GP regression for third system input

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Fig. 9

Estimated integrated limit state using GP regression for fourth system input

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Fig. 10

System reliability with respect to reliability levels

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Fig. 11

History of system reliability analysis for system input 1

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Fig. 12

History of system reliability analysis for system input 2

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Fig. 13

History of system reliability analysis for system input 3

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Fig. 14

History of system reliability analysis for system input 4

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