基于支持向量机的轨道结构病害识别研究

doi:10.16180/j.cnki.issn1007-7820.2022.02.005

Abstract

Abstract:

The track structure is a key component that carries the load of the train. Once a disease occurs, it will directly affect the safety of the train. To solve this problem, a method for identifying the track structure disease based on a support vector machine is proposed. This method uses time-domain statistics and discrete wavelet transform to perform joint feature extraction on the vibration acceleration data of the sleeper under different working conditions of the track structure, such as normal state, unsupported sleeper and cement hardening, which reduces the dimensionality of data and provides the possibility for disease identification. The method also uses the support vector machine algorithm to identify the feature vector, and uses the grid search method to select the parameters of the support vector machine, so that the recognition accuracy rate reaches about 85%. The experimental results show that the proposed method can better identify different degrees of unsupported sleeper and cement hardening, and provide a technical basis for online early warning of track structure failure.

Key words: unsupported sleeper, cement hardening, disease identification, time-domain statistics, feature extraction, grid search, support vector machine

CLC Number:

TP391.4

WU Weijia,YANG Jian,YUAN Tianchen,SHAO Zhihui. Research on Track Structure Damage Identification Based on Support Vector Machine[J].Electronic Science and Technology, 2022, 35(2): 27-33.

Figures/Tables 6

Figure 1.

Table 1.

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References 19

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工况编号	工况状态	刚度K_b	阻尼C_b
A1	正常	K_b	C_b
A2	轨枕空吊	0.5 K_b	0.5 C_b
A3	轨枕空吊	0	0
A4	道床板结	2 K_b	2 C_b
A5	道床板结	3 K_b	3 C_b

Research on Track Structure Damage Identification Based on Support Vector Machine

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