Condition-based maintenance using the inverse Gaussian degradation model

Nan Chen, Zhi Sheng Ye, Yisha Xiang, Linmiao Zhang

Research output: Contribution to journalArticlepeer-review

163 Scopus citations


Condition-based maintenance has been proven effective in reducing unexpected failures with minimum operational costs. This study considers an optimal condition-based replacement policy with optimal inspection interval when the degradation conforms to an inverse Gaussian process with random effects. The random effects parameter is used to account for heterogeneities commonly observed among a product population. Its distribution is updated when more degradation observations are available. The observed degradation level together with the unit's age are used for the replacement decision. The structure of the optimal replacement policy is investigated in depth. We prove that the monotone control limit policy is optimal. We also provide numerical studies to validate our results and conduct sensitivity analysis of the model parameters on the optimal policy.

Original languageEnglish
Pages (from-to)190-199
Number of pages10
JournalEuropean Journal of Operational Research
Issue number1
StatePublished - May 16 2015


  • Heterogeneity
  • Inverse Gaussian process
  • Optimal replacement


Dive into the research topics of 'Condition-based maintenance using the inverse Gaussian degradation model'. Together they form a unique fingerprint.

Cite this