Intelligent Diagnosis Method for Train Traction Motor Defects Based on Multimodal Fusion

By employing a multimodal fusion intelligent diagnostic method for train traction motor defects and utilizing polar coordinate image analysis, the problem of insufficient diagnostic accuracy and low efficiency in existing technologies has been solved. This enables intelligent, accurate, and efficient diagnosis of motor defects, ensuring train safety.

CN120891373BActive Publication Date: 2025-12-02BEIJING BEIJIUFANGKEMAO CO LTD
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Patent Information

Application Number
CN202511373684.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-09-25
Publication Date
2025-12-02
Estimated Expiration
2045-09-25

AI Technical Summary

Technical Problem

Existing methods for diagnosing defects in train traction motors rely on a single data source, resulting in insufficient diagnostic accuracy, susceptibility to human factors, delayed diagnosis, low efficiency, and difficulty in achieving intelligent processing.

Method used

A multimodal fusion intelligent diagnostic method for train traction motor defects is adopted. By monitoring the motor's operating status data, a list of benchmark and defect data is constructed, data mapping and feature calculation are performed, a polar coordinate image is constructed, and real-time monitoring and similarity analysis are conducted to diagnose motor defects.

Benefits of technology

It improves the accuracy of diagnosis, reduces the risk of misdiagnosis and missed diagnosis, enables early prevention of motor failures, ensures train safety, and optimizes data processing speed and efficiency.

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Abstract

This invention discloses an intelligent diagnostic method for train traction motor defects based on multimodal fusion, relating to the fields of pre-diagnosis and health management. It addresses the problem of insufficient accuracy in diagnosing train traction motor defects, and includes the following steps: Step S1: Acquire the motor's operating status data; Step S2: Acquire a baseline data list and a defect data list; calculate the defect data difference between the baseline data list and the defect data list, and map the defect data difference to obtain a mapped value; Step S3: Perform feature calculation to obtain the feature values ​​of the motor defects, and sort the various operating data of the motor according to the feature values; obtain the mapping interval of the mapped value, and construct a polar coordinate image by combining the mapped value and the sorting result; Step S4: Acquire the motor's real-time data; perform a similarity test between the real-time data image and the feature image of the defective motor; This invention can effectively improve the accuracy of motor defect diagnosis.
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