Train fault identification based on compressed sensing and deep wavelet neural network

doi:10.11772/j.issn.1001-9081.2018112278

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (7): 2175-2180.DOI: 10.11772/j.issn.1001-9081.2018112278

• Frontier & interdisciplinary applications • Previous Articles

Train fault identification based on compressed sensing and deep wavelet neural network

DU Xiaolei^1,2, CHEN Zhigang^1,2, ZHANG Nan¹, XU Xu^1,2

1. School of Mechanical-Electronic and Automobile Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2. Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Received:2018-11-14 Revised:2018-12-27 Online:2019-07-10 Published:2019-07-15
Supported by:
This work is partially supported by the National Natural Science Foundation of China (51605022), the Fundamental Research Funds for Beijing Universities (X18217), the General Project of Science and Technology Plan of Beijing Education Commission (SQKM201710016014), the Beijing Excellent Talents Training Program (2013D005017000013)

基于压缩感知和深度小波网络的列车故障识别

杜小磊^1,2, 陈志刚^1,2, 张楠¹, 许旭^1,2

1. 北京建筑大学机电与车辆工程学院, 北京 100044;
2. 北京建筑大学北京市建筑安全监测工程技术研究中心, 北京 100044

通讯作者: 陈志刚
作者简介:杜小磊(1993-),男,河北保定人,硕士研究生,主要研究方向:信号处理、深度学习、故障诊断;陈志刚(1979-),男,湖北黄冈人,副教授,博士,主要研究方向:信号处理、故障诊断;张楠(1979-),女,辽宁鞍山人,讲师,博士,主要研究方向:机械振动、非线性振动、动力学;许旭(1995-),男,宁夏固原人,硕士研究生,主要研究方向:信号处理、故障诊断。
基金资助:
国家自然科学基金资助项目（51605022）；北京市属高校基本科研业务费专项（X18217）；北京市教育委员会科技计划一般项目（SQKM201710016014）；北京市优秀人才培养资助项目（2013D005017000013）。

Abstract

Abstract:

Aiming at the difficulty of unsupervised feature learning on defect vibration data of train running part, a method based on Compressed Sensing and Deep Wavelet Neural Network (CS-DWNN) was proposed. Firstly, the collected vibration data of train running part were compressed and sampled by Gauss random matrix. Secondly, a DWNN based on improved Wavelet Auto-Encoder (WAE) was constructed, and the compressed data were directly input into the network for automatic feature extraction layer by layer. Finally, the multi-layer features learned by DWNN were used to train multiple Deep Support Vector Machines (DSVMs) and Deep Forest (DF) classifiers respectively, and the recognition results were integrated. In this method DWNN was employed to automatically mine hidden fault information from compressed data, which was less affected by prior knowledge and subjective influence, and complicated artificial feature extraction process was avoided. The experimental results show that the CS-DWNN method achieves an average diagnostic accuracy of 99.16%, and can effectively identify three common faults in train running part. The fault recognition ability of the proposed method is superior to traditional methods such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and deep learning models such as Deep Belief Network (DBN), Stack De-noised Auto-Encoder (SDAE).

Key words: train, Compressed Sensing (CS), Wavelet Auto-Encoder (WAE), deep learning, fault identification

摘要：

针对列车走行部故障振动数据无监督特征学习的难点，提出了一种基于压缩感知和深度小波神经网络（CS-DWNN）的列车故障识别方法。首先，对采集得到的列车走行部振动信号利用高斯随机矩阵进行压缩采样；其次，构建以改进小波自编码器（WAE）为基础的深层小波网络，将压缩后的信号直接输入网络进行自动逐层特征提取；最后，用DWNN学习到的多层特征分别训练多个深度支持向量机（DSVM）和深度森林（DF）分类器，并将识别结果进行集成。该方法利用深层小波网络从压缩信号中自动挖掘隐藏的故障信息，受先验知识和主观影响较小，并且避免了复杂的人工特征提取过程。实验结果表明，CS-DWNN方法取得了99.16%的平均诊断正确率，能够有效识别列车走行部的3种常见故障，识别能力优于传统的人工神经网络（ANN）、支持向量机（SVM）等方法和深度信念网络（DBN）、堆栈降噪自编码器（SDAE）等深度学习模型。

关键词: 列车, 压缩感知, 小波自编码器, 深度学习, 故障识别

CLC Number:

TP391.4
TP18

DU Xiaolei, CHEN Zhigang, ZHANG Nan, XU Xu. Train fault identification based on compressed sensing and deep wavelet neural network[J]. Journal of Computer Applications, 2019, 39(7): 2175-2180.

杜小磊, 陈志刚, 张楠, 许旭. 基于压缩感知和深度小波网络的列车故障识别[J]. 计算机应用, 2019, 39(7): 2175-2180.

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Train fault identification based on compressed sensing and deep wavelet neural network

基于压缩感知和深度小波网络的列车故障识别

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