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

80 Scopus citations

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 - Jan 1 2013

Keywords

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

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information'. Together they form a unique fingerprint.

  • Cite this