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The mechanical performance of high-speed train bogies has an important impact on the safety and comfort of the entire operating train system. The main purpose of this paper is to apply the intelligent signal processing methods such as wavelet entropy and support vector machine to the processing of massive online monitoring data of high-speed train bogies, seek the characteristic parameters that are generally applicable to the fault state of high-speed trains, and judge and identify the fault state of the main damping components of the bogies. This paper is aimed at discussing the applicability of wavelet entropy feature extraction method in vibration signal analysis of high-speed trains.

Feature Analysis of Mechanical and Electronic Signals Based on Grey Neural Network Prediction