Reliability and importance analysis of uncertain system with common cause failures based on survival signature. (September 2020)

• Record Type: Journal Article
• Authors: Mi, Jinhua; Beer, Michael; Li, Yan-Feng; Broggi, Matteo; Cheng, Yuhua
• Is Part Of: Reliability engineering & system safety. Volume 201(2020)
• Publication Date: 2020-09
• Subjects: Reliability analysis -- Survival signature -- Common cause failure -- Hierarchical Bayesian inference -- Epistemic uncertainty -- Dual-axis pointing mechanism; 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.2020.106988
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Possible CCF scenarios are modeled and quantified by decomposed partial α factors. Uncertainties for CCF events are reduced by hierarchical Bayesian inference method. Reliability of redundant uncertain system with CCFs is modeled by survival signature. Importance of components and CCF events are defined and ranked. The proposed method is effectively used to analyze the reliability of a satellite subsystem. Abstract: Redundant design has become the commonly used technique for ensuring the reliability of complex systems, which calls for great concern to common cause failure problems in such systems. Incomplete data in combination with vague judgments from experts introduce imprecision and epistemic uncertainties in the performance characterization of components. These issues need to be taken into account for assessing the system reliability. In this paper, a comprehensive reliability assessment method is presented by adopting the concept of survival signature to estimate the reliability of complex systems with multiple types of components. Particular attention is devoted to common cause failures (CCFs), which are modeled and quantified by decomposed partial α-decomposition method. Uncertainties caused by incomplete data for CCF events are reduced by hierarchical Bayesian inference. The component importance measure is enhanced to assess the importance of various possible CCF scenarios and to identify their potential impact on system reliability. The presented method is used to analyze the reliability of a dual-axis pointing mechanism for communication satellite, which is a commonly used satellite antenna control mechanism. The engineering application demonstrates the effectiveness of the method.