Southwest Jiaotong University

Zefeng Wen

Shulin Liang

Xinbiao Xiao

Qinglin Xie

Gongquan Tao

Xiaolong Liu

The present disclosure discloses a rail corrugation recognition method and apparatus based on a support vector machine, a device, and a medium. The method includes: obtaining wheel-rail noise signals in different time periods, and obtaining wheel-rail noise time domain information; dividing the wheel-rail noise time domain information into segmented wheel-rail noise time domain information corresponding to each of the different time periods; preprocessing each piece of segmented wheel-rail noise time domain information, and extracting a time domain statistical characteristic quantity and frequency domain eigenmode energy of each piece of segmented wheel-rail noise time domain information, to obtain a multi-dimensional wheel-rail noise characteristic vector; constructing a rail corrugation state recognition model based on a support vector machine, and training the rail corrugation state recognition model; and recognizing to-be-recognized wheel-rail noise data by using the rail corrugation state recognition model based on a support vector machine, to obtain a rail corrugation state.

Rail corrugation recognition method and apparatus based on support vector machine, device, and medium

An adaptive method of cleaning structural health monitoring (SHM) data based on local outlier factor is provided, including following steps: step 1: dividing SHM data to be analyzed into a series of data segments by using adaptive sliding window (ASW) technology; step 2: extracting time-domain statistical factors and frequency-domain statistical factors of each of the data segments to refine data information, thereby forming objects for study; step 3: calculating an outlier degree of each of the objects by using a weighted multiscale local outlier factor (WMLOF) based on feature factors; and step 4: detecting anomalies in SHM data by comparing a WMLOF value with a threshold value. The adaptive method can improve data quality effectively.

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Chengdu, China

Zefeng Wen