Fault Diagnosis of High-Speed Railway Track Grinding Motor Using Convolutional Neural Network

• Published in: 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) pp. 286 - 290
• Main Authors: Zhou, Qicai, Zhong, Xiaoyong, Xiong, Xiaolei, Zhao, Jiong, Huang, Jiewu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19-02-2024
• Subjects: Analytical models; auto-encoder; convolutional neural network; Costs; Fault diagnosis; motor; Motors; Rails; Training; vibration signal; Vibrations
• ISSN: 2831-6983
• DOI: 10.1109/ICAIIC60209.2024.10463208

Electric motors are common devices widely used in the industrial sector, making the study of motor fault diagnosis highly representative. In particular, for rail grinding vehicles, which play a significant role in the preventive maintenance and periodic upkeep of railway tracks, ensuring optimal train operation is of paramount importance. However, due to harsh operating conditions, the grinding motors on rail grinding vehicles frequently experience failures. Typically, these motors are periodically inspected and repaired by railway workers, which often leads to delayed handling of faulty motors, thereby compromising the efficiency of rail grinding operations and increasing the maintenance costs associated with motor repairs. Consequently, there is a need to investigate a fault diagnosis model for grinding motors and establish a system for remote fault diagnosis of these motors. To address this issue, the first step involves analyzing the maintenance records of grinding motors to identify common failure locations and types, and subsequently collecting corresponding vibration data. Next, a fault diagnosis model is developed based on the specific failure characteristics of grinding motors. This model is trained and optimized using a data set of vibration data from grinding motors to determine a suitable fault diagnosis model for this specific application. Finally, the developed fault diagnosis model for grinding motors is applied to diagnose faults in these motors, thereby validating the practical effectiveness of the model. By conducting this research, it is anticipated that a comprehensive understanding of the fault diagnosis process for grinding motors can be achieved, leading to the implementation of a remote fault diagnosis system for these motors. Ultimately, this will contribute to improved operational efficiency and reduced maintenance costs in rail grinding operations.