Reliability estimation from two types of accelerated testing data considering measurement error

Zhonghai Ma, Shaoping Wang, Cesar Ruiz, Chao Zhang, Haitao Liao, Edward Pohl

Research output: Contribution to journalArticle

Abstract

Reliability testing is an indispensable tool for evaluating the lifetime of a product. However, for a highly reliable product, it is quite common that a large proportion of test units will be censored in a regular life test or even in accelerated life testing (ALT) when the total testing time is too short. As an alternative, accelerated degradation testing (ADT) can be conducted to collect degradation data of a highly reliable product under accelerated conditions. For a reliability practitioner, it will be very valuable to use both ALT and ADT data for reliability estimation. In practice, degradation data are often contaminated by measurement error, which may affect the accuracy of reliability estimation. Therefore, a statistical procedure is needed when using both ALT data and ADT data with measurement error for evaluating the reliability of a highly reliable product. In this paper, an Inverse Gaussian (IG) process is used to model the degradation process of a product considering measurement error. To incorporate the two types of accelerated testing data, a new expectation-maximization (EM) algorithm is developed to estimate the model parameters by taking advantage of the parameter structure. A simulation study and a case study on a hydraulic piston pump are presented to illustrate the practical value of the proposed method in improving the accuracy of reliability estimation for a highly reliable product.

Original languageEnglish (US)
Article number106610
JournalReliability Engineering and System Safety
Volume193
DOIs
StatePublished - Jan 1 2020

Fingerprint

Maximum principle
Electron emission
Measurement errors
Inverse problems
Testing
Degradation
Reciprocating pumps
Hydraulics

Keywords

  • Accelerated degradation testing (ADT)
  • Accelerated life testing (ALT)
  • Expectation-maximization (EM)
  • Inverse Gaussian (IG) process
  • Measurement error
  • Reliability estimation

ASJC Scopus subject areas

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

Cite this

Reliability estimation from two types of accelerated testing data considering measurement error. / Ma, Zhonghai; Wang, Shaoping; Ruiz, Cesar; Zhang, Chao; Liao, Haitao; Pohl, Edward.

In: Reliability Engineering and System Safety, Vol. 193, 106610, 01.01.2020.

Research output: Contribution to journalArticle

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