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

A computer device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the computer program is executed by the processor, a rail corrugation recognition method based on a support vector machine is implemented;

• wherein the method comprises:
• obtaining wheel-rail noise signals in different time periods, and obtaining wheel-rail noise time domain information according to the wheel-rail noise signals;
• dividing the wheel-rail noise time domain information into segmented wheel-rail noise time domain information corresponding to each of the different time periods, wherein each piece of segmented wheel-rail noise time domain information corresponds to an equal length of a moving path of an urban rail transit vehicle;
• 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;
• obtaining a multi-dimensional wheel-rail noise characteristic vector according to the time domain statistical characteristic quantity and the frequency domain eigenmode energy;
• constructing a rail corrugation state recognition model based on a support vector machine according to the multi-dimensional wheel-rail noise characteristic vector, and training the rail corrugation state recognition model based on a support vector machine;
• 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.

A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a rail corrugation recognition method based on a support vector machine is implemented;

• wherein the method comprises:
• obtaining wheel-rail noise signals in different time periods, and obtaining wheel-rail noise time domain information according to the wheel-rail noise signals;
• dividing the wheel-rail noise time domain information into segmented wheel-rail noise time domain information corresponding to each of the different time periods, wherein each piece of segmented wheel-rail noise time domain information corresponds to an equal length of a moving path of an urban rail transit vehicle;
• 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;
• obtaining a multi-dimensional wheel-rail noise characteristic vector according to the time domain statistical characteristic quantity and the frequency domain eigenmode energy;
• constructing a rail corrugation state recognition model based on a support vector machine according to the multi-dimensional wheel-rail noise characteristic vector, and training the rail corrugation state recognition model based on a support vector machine;
• 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.