Turnouts are crucial to the safety of high-speed railways. Due to the intensive use and complex environment, breakdowns caused by different faults occur frequently in practice. Considering that the operation of turnouts is a multi-stage process during which each stage has its specific health characteristics, this paper proposes segmentalized maximal-relevancy and minimal-redundancy (mRMR) for feature extraction from each stage separately. Based on mathematical analysis of the turnout mechanism, the electric power curve is segmented into four stages, from which time-domain analysis and mRMR are combined to extract valid features corresponding to different movements respectively. Then, a novel classifier named cost-sensitive Extreme Learning Machine with fixed inputs (cf-ELM) is proposed for fault classification. We modify the inputs of ELM and define a new formula to limit the input weights and biases for the sake of stability of the network structure. Besides, a cost-sensitive optimization method is also presented in this classifier to embed the failure degree and data proportion into cost calculation rules to deal with data imbalance. To verify our proposed method, real data collected from a turnout of Beijing-Shanghai high-speed railway is used. It is proven by comparisons that the accuracy of our method has achieved 100% with fast running speed and also outperforms traditional methods in terms of stability and generalization remarkably.
Segmentalized mRMR Features and Cost-Sensitive ELM With Fixed Inputs for Fault Diagnosis of High-Speed Railway Turnouts
IEEE Transactions on Intelligent Transportation Systems ; 24 , 5 ; 4975-4987
2023-05-01
3594508 byte
Article (Journal)
Electronic Resource
English
A Data-Driven Fault Diagnosis Method for Railway Turnouts
Transportation Research Record | 2019
|Online Contents | 1994
Geometry of High-Speed Turnouts
Online Contents | 2011
|Geometry of High-Speed Turnouts
Transportation Research Record | 2011
|