Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective

Weiwen Peng, Yan-Feng Li, Jinhua Mi, Le Yu and Hong-Zhong Huang

Reliability Engineering and System Safety, 2016, vol. 153, issue C, 75-87

Abstract: Degradation analysis is critical to reliability assessment and operational management of complex systems. Two types of assumptions are often adopted for degradation analysis: (1) single degradation indicator and (2) constant external factors. However, modern complex systems are generally characterized as multiple functional and suffered from multiple failure modes due to dynamic operating conditions. In this paper, Bayesian degradation analysis of complex systems with multiple degradation indicators under dynamic conditions is investigated. Three practical engineering-driven issues are addressed: (1) to model various combinations of degradation indicators, a generalized multivariate hybrid degradation process model is proposed, which subsumes both monotonic and non-monotonic degradation processes models as special cases, (2) to study effects of external factors, two types of dynamic covariates are incorporated jointly, which include both environmental conditions and operating profiles, and (3) to facilitate degradation based reliability analysis, a serial of Bayesian strategy is constructed, which covers parameter estimation, factor-related degradation prediction, and unit-specific remaining useful life assessment. Finally, degradation analysis of a type of heavy machine tools is presented to demonstrate the application and performance of the proposed method. A comparison of the proposed model with a traditional model is studied as well in the example.

Keywords: Multivariate degradation process; Inverse Gaussian process; Copula function; Dynamic covariates; Bayesian reliability (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:153:y:2016:i:c:p:75-87

DOI: 10.1016/j.ress.2016.04.005

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