Adaptive monitoring scheme of stochastically failing systems under hidden degradation processes. (May 2022)

• Record Type: Journal Article
• Authors: Duan, Chaoqun; Li, Yifan; Pu, Huayan; Luo, Jun
• Is Part Of: Reliability engineering & system safety. Volume 221(2022)
• Publication Date: 2022-05
• Subjects: Adaptive monitoring -- Prognostics and health management (PHM) -- Fault prediction -- Semi-Markov decision process (SMDP); Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108322
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Development of an adaptive monitoring scheme for fault detection. Multivariate hidden Markov model describes the deterioration process. SMDP approach to formulate the control problem and to optimize the scheme. Considerably better fault detection compared with published results. Abstract: Limited by the installation of sensors and the structure of systems, only external observation information can generate an association with the internal health status of a system. In addition, modern industrial systems are often accompanied by hidden degradation processes, which present great challenges when monitoring and alerting impending failures. To address this issue, this paper proposes an adaptive monitoring scheme for predicting faults of systems with hidden degradation processes. A multivariate, continuous-time hidden Markov model with three states (hidden healthy state, hidden unhealthy state, and observable failure state) is established to describe the degradation process of the system. An expectation maximization (EM) algorithm is presented to estimate the parameters of the stochastic model. On the basis of the hidden degradation model, an adaptive Bayesian control scheme is developed for monitoring the potential risk of the system with two switchable sampling intervals. We calculate optimal decision variables of the control scheme to achieve the minimum expected average cost by a policy iteration algorithm under a semi-Markov decision process (SMDP). The performance and characteristics of the proposed scheme are demonstrated via real oil measurement data obtained from mechanical generators. A comparison case illustrates the superiority of the prediction performance of our monitoring scheme.