Abstract
INTRODUCTION: With the development of technology and policy support, high-speed rail's temporal and spatial layout is gradually expanding, and it becomes essential to ensure high-safety operation.
 OBJECTIVES: The real-time correlation fault diagnosis technology of critical components of electromechanical systems of high-speed trains is analyzed, and a new method of automatic fault diagnosis based on genetic support vector machine is proposed.
 METHODS: In this study, the Author combines two techniques, IFD and AE, and introduces an adaptive weighting algorithm to fuse the data of the two and experimentally verify their accuracy.
 RESULTS: The experimental results show that in the IFD experiment, the 2-point frequency at 1050 speed is 347.6 Hz, and the 3-point frequency is 498.4 Hz, both of which are very close to the 2 and 3 times frequencies of the 1-point frequency, and the multiplicative relationship is much more straightforward.
 CONCLUSION: Combining IFD and AE can realize automatic and accurate diagnosis of bearing state and pre-diagnosis of bearings by adaptive weighted fusion algorithm, which is effective in the practical mechanical diagnosis of rolling bearing faults in high-speed railroads.
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