A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information

Lizhi Wang, Rong Pan, Xiaoyang Li, Tongmin Jiang

Research output: Contribution to journalArticle

75 Citations (Scopus)

Abstract

Accelerated degradation testing (ADT) is a common approach in reliability prediction, especially for products with high reliability. However, oftentimes the laboratory condition of ADT is different from the field condition; thus, to predict field failure, one need to calibrate the prediction made by using ADT data. In this paper a Bayesian evaluation method is proposed to integrate the ADT data from laboratory with the failure data from field. Calibration factors are introduced to calibrate the difference between the lab and the field conditions so as to predict a product's actual field reliability more accurately. The information fusion and statistical inference procedure are carried out through a Bayesian approach and Markov chain Monte Carlo methods. The proposed method is demonstrated by two examples and the sensitivity analysis to prior distribution assumption.

Original languageEnglish (US)
Pages (from-to)38-47
Number of pages10
JournalReliability Engineering and System Safety
Volume112
DOIs
StatePublished - 2013

Fingerprint

Reliability Evaluation
Evaluation Method
Degradation
Testing
Information fusion
Predict
Information Fusion
Markov processes
Prediction
Sensitivity analysis
Markov Chain Monte Carlo Methods
Bayesian Methods
Prior distribution
Statistical Inference
Monte Carlo methods
Bayesian Approach
Calibration
Sensitivity Analysis
Integrate

Keywords

  • Bayesian inference
  • Degradation analysis
  • Information fusion model
  • Sensitivity analysis

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality
  • Applied Mathematics

Cite this

A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information. / Wang, Lizhi; Pan, Rong; Li, Xiaoyang; Jiang, Tongmin.

In: Reliability Engineering and System Safety, Vol. 112, 2013, p. 38-47.

Research output: Contribution to journalArticle

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