Adaptive stochastic-filter-based failure prediction model for complex repairable systems under uncertainty conditions. (December 2020)

• Record Type: Journal Article
• Authors: Yizhen, Peng; Yu, Wang; Jingsong, Xie; Yanyang, Zi
• Is Part Of: Reliability engineering & system safety. Volume 204(2020)
• Publication Date: 2020-12
• Subjects: Repairable systems -- Failure prediction -- Multiple uncertainties -- Bayesian recursive filter -- EM algorithm; 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.2020.107190
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A new failure prediction model considering multiple uncertainties was proposed. The proposed model can adaptively estimate the prior distribution of ROCOF. The proposed model can adaptively adjust model parameters. A case study for compressor was used to verify the effectiveness of the model. Abstract: Dynamical reliability assessment and failure prediction are effective tools for ensuring the efficiency, availability, and safety of repairable systems. To achieve better assessment performance, accurate modeling failure recurrence data are the core of prediction approaches. However, because of the uncertainties from the environmental conditions and repair activities, the failure counting model is usually not well established. To solve this problem, in this paper, we propose an adaptive recursive-filter-based dynamical failure prediction approach for complex repairable systems. First, based on the framework of the state space model, a fusion model that fuses Brownian motion into a nonhomogeneous Poisson process is proposed to characterize failure process under multiple uncertainty conditions. Then, an adaptive statistical inference method based on a Bayesian recursive filter and the EM algorithm is derived to update the model parameters and estimate the initial states adaptively. To verify the effectiveness of the proposed approach, a real gas pipeline compressors reliability prediction problem was implemented.