Skip to main navigation Skip to search Skip to main content

Reliability Analysis Based on a Bivariate Degradation Model Considering Random Initial State and Its Correlation with Degradation Rate

  • Harbin Institute of Technology
  • Wenzhou University

Research output: Contribution to journalArticlepeer-review

Abstract

Dependent degradation processes of performance characteristics are ubiquitous in engineered systems. The initial state of each degradation process is usually random and relates to the degradation rate. Unfort unately, most existing Wiener-based bivariate degradation models fail to consider these two features, which may limit the accuracy of system reliability assessment. In order to surmount this limitation, in this article, we develop a new bivariate degradation model, which involves a generalized Wiener process-based marginal degradation model that considers the random initial state and the correlation between the initial state and the degradation rate and a corresponding degradation increment-based dependence structure (DS). The proposed bivariate degradation model and its reliability function are derived first. Then, a two-stage statistical inference is introduced using the maximum likelihood estimation method. Furthermore, a simulation study investigates the performance of the statistical inference and the misspecification effects of DSs and marginal degradation models. Finally, an illustrative example demonstrates the effectiveness of the proposed model.

Original languageEnglish
Pages (from-to)37-48
Number of pages12
JournalIEEE Transactions on Reliability
Volume72
Issue number1
DOIs
StatePublished - 1 Mar 2023

Keywords

  • Bivariate degradation model
  • correlation between initial state and degradation rate (CISDR)
  • dependence structure (DS)
  • random initial state
  • reliability analysis

Fingerprint

Dive into the research topics of 'Reliability Analysis Based on a Bivariate Degradation Model Considering Random Initial State and Its Correlation with Degradation Rate'. Together they form a unique fingerprint.

Cite this