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Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach

Wencheng Huang, Xingyi Kou, Yue Zhang, Rongwei Mi, Dezhi Yin, Wei Xiao and Zhanru Liu

Reliability Engineering and System Safety, 2021, vol. 205, issue C

Abstract: In this paper, a Bayesian Network-K2 Algorithm-Expectation Maximization (BN-K2-EM) approach is proposed to quantify the intensity of coupling influence among the operational failures and find out the specific failure propagation chains in accidents of high-speed electric multiple units, quantitatively. K2 is applied to learn the BN structure because it can effectively learn the structure based on small scale data sets, EM is applied to learn the parameter in BN because it can accurately reflect the probability value among nodes with fast convergence speed. BN-K2-EM belongs to a data-driven method which overcomes the limitation of the logic-based BN approach. BN-K2-EM has 6 steps: establish the failures data matrix; determine the priority of nodes priority based on expanded average causal effect; learn the structure of BN based on K2 algorithm; learn the parameter of BN based on EM algorithm; causal reasoning and predication; reverse reasoning and diagnosis. Finally, a case study is conducted by using the historical high-speed EMU accidents happened in China from 2008 to 2019 as background. The influence strength and sensitivity of each failure mode in the case study are analyzed, and the sensitivity analysis of nodes in each system or sub-system is also conducted.

Keywords: High-speed electric multiple units; Operational failure analysis; Bayesian network; K2 algorithm; Expectation maximization; Failure propagation chain (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:205:y:2021:i:c:s095183202030750x

DOI: 10.1016/j.ress.2020.107250

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