计算机科学 ›› 2016, Vol. 43 ›› Issue (12): 281-286.doi: 10.11896/j.issn.1002-137X.2016.12.052
郭超,杨燕,金炜东
GUO Chao, YANG Yan and JIN Wei-dong
摘要: 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。
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