基于EDBN-SVM的高速列车故障分析

Fault Analysis of High Speed Train Based on EDBN-SVM

深度学习作为机器学习领域的新热点,为故障诊断技术领域的研究开拓了新的思路。针对高速列车进行故障分析的重要性,将深度学习和集成学习相结合,提出一种基于EDBN-SVM(EnsembleDeep Belief Network-Support Vector Machine)的故障诊断模型。首先对高速列车振动信号进行快速傅立叶变换,其次分析确定了EDBN-SVM模型的参数,然后将信号的FFT系数作为EDBN-SVM模型的可视层输入,并逐层学习高层特征,最后利用多个SVM分类器进行识别并对识别结果进行集成。为评估该方法的有效性,采用实验室数据和仿真数据进行实验测试,并与传统的几种故障分析方法进行对比。结果表明,该方法的故障识别效果优于传统的故障分析方法,同时稳定性更好。

As a new hot spot in the field of machine learning,deep learning has opened up new ideas for the research of fault diagnosis. In view of significance of fault analysis for high speed train, combining deep learning and ensemble learning, a new fault diagnosis model based on EDBN-SVM（Ensemble Deep Belief Network-Support Vector Machine）was proposed. Firstly, we preprocessed the vibration signal of high speed train by fast fourier transform （FFT）. Secondly, we analyzed the parameters of the EDBN-SVM model, then we set the FFT coefficients as the input of the visible layer of EDBN-SVM model, and used the model to learn high-level features layer by layer. Finally, we utilized multiple SVM classifiers to recognize faults, and combined the recognition results. In order to evaluate the validity of this method, we selected the laboratory data and the simulation data to conduct experiments, and compared it with the traditional fault analysis methods. The results show that the fault recognition effect and the stability of this method are better than traditional methods.