Solid Mechanics and Vehicle Conceptual Design

Selective maintenance optimization for multi-state systems subject to common cause failures

  • CAO Wenbin ,
  • JIA Xisheng ,
  • HU Qiwei
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  • Department of Management Engineering, Ordnance Engineering College, Shijiazhuang 050003, China

Received date: 2017-06-26

  Revised date: 2017-07-23

  Online published: 2017-07-21

Supported by

National Natural Science Foundation of China (71401173)

Abstract

Common Cause Failure (CCF) exists extensively in the Multi-State System (MSS). It is of great significance to investigate the maintenance optimization for the MSS subject to CCF to improve the probability of mission success and reduce the failure cost of the system. Taking into account the fuzzy properties of the multi-state system, mission profile and Random Common Cause Failure(RCCF), an analytic model (AM) is proposed to evaluate the mission success probability of the fuzzy multi-state system (FMSS) subject to RCCF based on the Fuzzy Markov Model (FMM) and Fuzzy Universal Generating Function (FUGF), and its validity is verified by the well-known Monte Carlo Simulation (MCS) method. Based on the proposed model, a selective maintenance optimization model for the FMSS considering the maintenance cost constraint is developed, and the optimal maintenance option is obtained. An illustrative example is presented to demonstrate the effectiveness of the proposed selective maintenance model, and the effects of the RCCF on the results of selective maintenance optimization and system state probability are analyzed. The results show that the RCCF and the α-cut level of the fuzzy number may affect the result of selective maintenance optimization, and neglecting the RCCF will cause overestimate of the mission success probability of the system.

Cite this article

CAO Wenbin , JIA Xisheng , HU Qiwei . Selective maintenance optimization for multi-state systems subject to common cause failures[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018 , 39(2) : 221556 -221556 . DOI: 10.7527/S1000-6893.2017.221556

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