Case Study on FMECA and Risk Assessment for the Door System in High Speed Train

Lu, Bi Hong; Peng, Hua; Fu, Zhen Liang; Gao, Feng

doi:10.4028/www.scientific.net/amr.655-657.2335

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…Due to the limitations of their attributes, they cannot identify a certain fault type. Failure mode and effects analysis (FMEA) is used to evaluate the reliability of high-speed train door system [8]. Bayesian network model using expert system knowledge is developed to diagnose the faults of sliding plug doors [9].…”

Section: Introductionmentioning

confidence: 99%

Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors

Sun

Xie

Cao

et al. 2018

Sensors

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As the only entry/exit for passengers getting on and off a train, the train plug door is of great importance to keep train operation safe and reliable. As signal processing technologies develop rapidly, taking the easy acquisition advantages of sound signals, a novel fault diagnosis method for train plug doors using multi-scale normalized permutation entropy (MNPE) and an improved particle swarm optimization based multi-class support vector machine (IPSO-MSVM) is proposed. Firstly, sound samples are collected using high-precision audio sensor. In the features extraction process, a hybrid method blending empirical mode decomposition (EMD), multi-scale permutation entropy (MNPE) with Fisher discrimination criterion is utilized. First, EMD is used to decompose each sound signal into several intrinsic mode functions (IMFs) and a residue for stationary processing. Then, MNPE features are extracted from the IMFs. To obtain the most significant features, the Fisher discrimination criterion is further applied. To address the time-consuming defects of traditional grid based method for selecting the optimal parameters of multi-class SVM, an improved PSO (IPSO) is proposed. The superiority of the IPSO-MSVM model and the hybrid feature extraction method was tested on the collected sound samples by comparing to commonly applied methods. Results indicate the identification accuracy of the proposed method is highest, which reaches 90.54%, demonstrating its feasibility.

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Section: Introductionmentioning

confidence: 99%

Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors

Sun

Xie

Cao

et al. 2018

Sensors

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“…Pliego and Garcia 13 utilized the Birnbaum and criticality importance measures of fault tree events to perform the prognostic evaluation and diagnosis of point mechanisms. Lu et al 14 identified key causes for a high-speed train door system by analyzing the criticality. Castillo and Grande 15 ranked the most critical causes of railway line accidents by using Bayesian network.…”

Section: Introductionmentioning

confidence: 99%

An importance order analysis method for causes of railway signaling system hazards based on complex networks

Liu

Zheng

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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The importance order of causes of a railway signaling system hazard is important for the arrangement of control measures for eliminating or controlling the system hazard. In order to obtain a reasonable importance order, a novel importance order analysis method based on complex networks is proposed in this article. In this article, to characterize the causes from a topological perspective, a comprehensive topology characteristic is proposed. It is built on the top of three typical topology characteristics with the weighted Euclidean distance. Then, an influence coefficient matrix is proposed to measure the causal influence between causes. Based on the proposed topology characteristic and matrix, an algorithm is designed to identify the importance order of causes. To verify the effectiveness of the proposed method, a case study on a Chinese high-speed railway signaling system hazard is performed. The results show that the proposed method is feasible for the arrangement of control measures to eliminate or control system hazards and performs better than the traditional complex network-based methods in terms of avoiding conflicting and rough results.

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Fault diagnosis for train plug door using weighted fractional wavelet packet decomposition energy entropy

Sun

Cao

2022

Accident Analysis & Prevention

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Case Study on FMECA and Risk Assessment for the Door System in High Speed Train

Cited by 7 publications

References 2 publications

Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors

Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors

An importance order analysis method for causes of railway signaling system hazards based on complex networks

Fault diagnosis for train plug door using weighted fractional wavelet packet decomposition energy entropy

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