A dynamic failure mode and effects analysis for train systems failures risk assessment using FCM and prospect theory

Abstract

Improving the reliability of railway train systems and preventing potential failures in the train operation process is one of the most significant tasks. The failure mode and effects analysis (FMEA) is the most effective and widely applied technique for identification, evaluation, and prevention risk of potential failures in diverse fields. Nevertheless, current risk prioritization approaches for FMEA overlook the transfer of decision makers’ risk preferences under different risk states of potential failures. In addition, little attrition has been paid to addressing the risk prioritization problems in FMEA under a dynamic environment. In order to bridge these research gaps, this paper proposes a dynamic prioritization approach for FMEA by integrating the Fuzzy Cognitive Map (FCM) and the prospect theory. First, improved weighted arithmetic averaging (WAA) operator based on the similarity measure is constructed to aggregate each decision maker’s evaluation information. Then, the FCM is applied to obtain the risk matrix and interaction relationships among failures under different risk states. Next, the dynamic prospect theory is built to determine the risk priority of each failure by considering the risk preference of decision makers, in which the dynamic weight functions are derived based on the risk matrix under different risk states. Finally, the proposed dynamic risk prioritization approach for FMEA is tested by the failures risk analysis of the railway train bogie system in the railway train systems. The comparison study is conducted to demonstrate the reliability and rationality of the proposed risk prioritization approach.