Key components identification of EMU complex system faults with interval intuitionistic fuzzy set and multi-attribute group decision-making based on FMECA method

IF 1.7 4区 工程技术 Q3 ENGINEERING, INDUSTRIAL
Yuchen Zhang, Jinghui Liu, Chengye Dai, Qiufen Li, Zhan Guo, Xianchun Dai
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Abstract

With the continuous acceleration of high-speed railway, the high-voltage traction system of the EMU is an important part for ensuring the operation speed and safety. If the failure does not discontinued effectively, it will cause major dangerous accidents, so the key components identification of system is crucial. This paper focus on the contradictions of the expert evaluation information ambiguity, the difference of expert risk appetite and the rationality of risk priority number (RPN) calculation method in the traditional failure analysis method FMECA. The interval intuitionistic fuzzy set (IIFS) is introduced to transform the expert evaluation into the form of membership interval and non-membership interval, which reduced the ambiguity of the specific numerical score. The interval intuitive fuzzy entropy was used to determine the entropy values of the occurrence (O), severity (S), and undetectable degree (D) of each failure mode under each expert score, which was used to calculate the weight value [Formula: see text], to weaken the influence caused by subjective risk preference. The interval intuition fuzzy ensemble operator (AIVIFWM) is used to assemble a single scoring matrix into a comprehensive score, which weakens the subjective influence of expert evaluation. Combined with the multi-attribute group decision-making idea, the score function [Formula: see text] is calculated for each comprehensive evaluation interval of each failure mode after assembly, so as to sort the failure mode risk and finally identify the key components. Based on the fault data of the high-voltage traction system of a certain type of EMU in 2022, 39 failure modes of 30 components are researched and summarized. The results show that rectifier, converter cooling unit, and carbon skateboard are the key components of EMU high-voltage traction system, which provided basic support for the detection and maintenance decision.
基于 FMECA 方法的区间直觉模糊集和多属性群决策的 EMU 复杂系统故障的关键部件识别
随着高速铁路发展的不断加快,动车组的高压牵引系统是保证动车组运行速度和安全的重要组成部分。如果故障不能有效排除,将会造成重大危险事故,因此系统关键部件的识别至关重要。本文重点研究了传统故障分析方法 FMECA 中专家评价信息模糊性、专家风险偏好差异性和风险优先级数(RPN)计算方法合理性的矛盾。引入区间直观模糊集(IIFS),将专家评价转化为成员区间和非成员区间的形式,减少了具体数值打分的模糊性。利用区间直观模糊熵来确定每种专家评分下每种故障模式的发生率(O)、严重程度(S)和不可检测度(D)的熵值,并以此计算权重值[公式:见正文],以弱化主观风险偏好造成的影响。利用区间直觉模糊集合算子(AIVIFWM)将单一评分矩阵集合为综合评分,弱化专家评价的主观影响。结合多属性分组决策思想,对组装后的每种故障模式的每个综合评价区间计算得分函数[公式:见正文],从而对故障模式风险进行排序,最终确定关键部件。基于 2022 年某型动车组高压牵引系统的故障数据,对 30 个部件的 39 种故障模式进行了研究和总结。结果表明,整流器、变流器冷却单元、碳滑板是动车组高压牵引系统的关键部件,为检测和维修决策提供了基础支撑。
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来源期刊
CiteScore
4.50
自引率
19.00%
发文量
81
审稿时长
6-12 weeks
期刊介绍: The Journal of Risk and Reliability is for researchers and practitioners who are involved in the field of risk analysis and reliability engineering. The remit of the Journal covers concepts, theories, principles, approaches, methods and models for the proper understanding, assessment, characterisation and management of the risk and reliability of engineering systems. The journal welcomes papers which are based on mathematical and probabilistic analysis, simulation and/or optimisation, as well as works highlighting conceptual and managerial issues. Papers that provide perspectives on current practices and methods, and how to improve these, are also welcome
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