Failure analysis of repairable systems with k-out-of-m configuration considering with common cause failure and maintenance correlation based on goal oriented method. (October 2022)

• Record Type: Journal Article
• Authors: Yi, Xiao-Jian; Xu, Chen-Hao; Liu, Shu-Lin; Xing, Man-Xi; Mu, Hui-na
• Is Part Of: Engineering failure analysis. Volume 140(2022)
• Publication Date: 2022-10
• Subjects: Failure analysis -- GO methodology -- Common Cause Failure -- K-out-of-m configuration -- Maintenance correlation; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106615
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: This study aims to develop an improved GO algorithm considering the correlation of the CCF and MC for reliability analysis of repairable systems. The Markovian reliability model is usually presented to estimate the failure probability with the CCF and MC. The new GO methodology is applied in analysising the dynamic unavailability of Hydraulic Oil Supply System (HOSS) of heavy vehicles. Abstract: System reliability directly affects the application of Prognostic and Health Management (PHM) technology. This paper proposed a new Goal Oriented (GO) methodology for failure probability analysis of repairable systems with the Common Cause Failure (CCF) and maintenance correlation (MC). It should be noted that the considered repariable systems in this study are k -out-of- m configuration. Firstly, an improved GO algorithm solving the CCF is elaborated. Second, a failure analysis framework for repairable systems taken into account of CCF and MC is developed based on Markov theory. Finally, the new GO methodology is applied in analysising the dynamic unavailability of Hydraulic Oil Supply System (HOSS) of heavy vehicles. Comparisons with two traditional methods indicated that the proposed methodology provides higher efficiency. Furthermore, this work can widen the application of GO methodology.