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Research Papers

Bayesian Reliability Analysis With Evolving, Insufficient, and Subjective Data Sets

[+] Author and Article Information
Pingfeng Wang

 University of Maryland, College Park, MD 20742pfwang@umd.edu

Byeng D. Youn

 University of Maryland, College Park, MD 20742bdyoun@umd.edu

Zhimin Xi

 University of Maryland, College Park, MD 20742zxi@umd.edu

Artemis Kloess

R&D Center, General Motors Corp., Warren, MI 48090artemis.kloess@gm.com

J. Mech. Des 131(11), 111008 (Oct 13, 2009) (11 pages) doi:10.1115/1.4000251 History: Received August 02, 2008; Revised August 17, 2009; Published October 13, 2009

This paper presents a new paradigm of system reliability prediction that enables the use of evolving, insufficient, and subjective data sets. The data sets can be acquired from expert knowledge, customer survey, inspection and testing, and field data throughout a product life-cycle. In order to handle such data sets, this research integrates probability encoding methods to a Bayesian updating mechanism. The integrated tool is called Bayesian Information Toolkit. Subsequently, Bayesian Reliability Toolkit is presented by incorporating reliability analysis to the Bayesian updating mechanism. A generic definition of Bayesian reliability is introduced as a function of a predefined confidence level. This paper also finds that there is no data-sequence effect on the updating results. It is demonstrated that the proposed Bayesian reliability analysis can predict the reliability of door closing performance in a vehicle body-door subsystem, where available data sets are insufficient, subjective, and evolving.

Copyright © 2009 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Process of Bayesian updating

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Figure 2

Results of the temperature survey

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Figure 3

Bayesian updating of the PDF of θ

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Figure 4

Confidence level versus sample size for Bayesian reliability

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Figure 5

PDFs for G(X1,X2) and G0

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Figure 6

Actual reliability and estimated reliability distribution

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Figure 7

Bayesian reliability for the mathematical example

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Figure 8

Vehicle door system

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Figure 9

Customer rejection rate

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Figure 10

Bayesian updating for the marginal velocity using a normal distribution

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Figure 11

Bayesian model for the marginal velocity using a normal distribution

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Figure 12

Bayesian reliability with 55 sets data (by MCS)

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Figure 13

Updated Bayesian reliability with 24 new data sets (by MCS)

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Figure 14

Bayesian reliability with 55 data sets (by EDR)

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Figure 15

Updated Bayesian reliability with 24 new data sets (by EDR)

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