Prior LDA and SVM Based Fault Diagnosis of Vehicle On-board Equipment for High Speed Railway

Author(s):  
Feng Wang ◽  
Tian-hua Xu ◽  
Yang Zhao ◽  
Ye-ran Huang
Author(s):  
Linggang Kong ◽  
Shuo Li ◽  
Xinlong Chen ◽  
Hongyan Qin

Vehicle on-board equipment is the most important train control equipment in high-speed railways. Due to the low efficiency and accuracy of manual detection, in this paper, we propose an intellectualized fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) network. Firstly, we collect the fault information sheets that are recorded by electrical personnel, using frequency weighting factor and principal component analysis (PCA) to realize the data extraction and dimension reduction; Then, in order to improve the fault diagnosis rate of the model, using genetic algorithm (GA) to optimize the parameters of the ANFIS network; Finally, using the fault data of a high-speed railway line in 2019 to test the model, the optimized ANFIS model can achieve 96% fault diagnosis rate for vehicle on-board equipments, which indicating the method is effective and accurate.


2016 ◽  
Vol 66 ◽  
pp. 407-420 ◽  
Author(s):  
Bing Zhang ◽  
Andy C.C. Tan ◽  
Jian-hui Lin

2013 ◽  
Vol 16 (1) ◽  
pp. 26-31 ◽  
Author(s):  
Hae Young Ji ◽  
Kang Ho Lee ◽  
Jae Chul Kim ◽  
Dong Hyoung Lee ◽  
Kyoung Ho Moon

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