基于双通道特征融合的轴承故障诊断方法

张晓宁; 朱慧龙; 辛亮; 杨慕晨; 汪浩

doi:10.13890/j.issn.1000-128X.2023.06.005

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基于双通道特征融合的轴承故障诊断方法

铁道机车车辆 | 更新时间：2024-08-02

- 基于双通道特征融合的轴承故障诊断方法
- Bearing fault diagnosis method based on dual-channel feature fusion
- 机车电传动 2023年第6期页码：39-48
- 作者机构：
  
  1.中车青岛四方机车车辆股份有限公司，山东青岛 266111
  2.西南交通大学轨道交通运载系统全国重点实验室，四川成都;610031
- 作者简介：
  
  汪　浩（1997—），男，硕士研究生，主要从事机车车辆测控技术研究和故障诊断；E‒mail: wh15223389523@163.com
- 基金信息：
  
  四川省自然科学基金项目(2022NSFSC1918;2022NSFSC1910)
- DOI：10.13890/j.issn.1000-128X.2023.06.005
  中图分类号： TH133.3;U292.91⁺4
- 纸质出版日期：2023-11-10，
  
  收稿日期：2023-08-17，
  
  修回日期：2023-10-25，
- 稿件说明：
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张晓宁, 朱慧龙, 辛亮, 等. 基于双通道特征融合的轴承故障诊断方法[J]. 机车电传动, 2023(6): 39-48.

ZHANG Xiaoning, ZHU Huilong, XIN Liang, et al. Bearing fault diagnosis method based on dual-channel feature fusion[J]. Electric drive for locomotives,2023(6): 39-48.
张晓宁, 朱慧龙, 辛亮, 等. 基于双通道特征融合的轴承故障诊断方法[J]. 机车电传动, 2023(6): 39-48. DOI： 10.13890/j.issn.1000-128X.2023.06.005.

ZHANG Xiaoning, ZHU Huilong, XIN Liang, et al. Bearing fault diagnosis method based on dual-channel feature fusion[J]. Electric drive for locomotives,2023(6): 39-48. DOI： 10.13890/j.issn.1000-128X.2023.06.005.

摘要

基于卷积神经网络（Convolution Neural Network，CNN）的智能诊断方法在轴承故障诊断中应用广泛，但是大多数诊断模型以单源信息输入为主，这将影响基于CNN的故障诊断准确性和可靠性。针对这个问题，文章提出一种基于双通道特征融合的滚动轴承故障诊断方法。首先利用多重Q因子连续Gabor小波变换（Multiple Q-factor Continuous Gabor Wavelet Transform，CMQGWT）和快速谱相干（Fast Spectral Coherence，Fast-SC）分别构造滚动轴承振动信号的时频分析图；然后搭建1个具有双输入通道的CNN网络模型，通过特征融合层将各个通道提取的深度时频特征融合成1个新的特征；最后利用分类器输出诊断结果。在高速列车滚动轴承单故障和复合故障的分类识别试验中，较之于单输入通道的CNN模型，该模型具有更高的诊断准确性和鲁棒性。

Abstract

Intelligent diagnosis method based on convolution neural network (CNN) has been widely used in bearing fault diagnosis. However

most existing diagnostic models rely on single-source information inputs

limiting their accuracy and reliability. To solve this limitation

this paper presents a rolling bearing fault diagnosis method based on dual-channel feature fusion. Firstly

the time-frequency analysis diagrams of rolling bearing vibration signals were constructed by using multiple Q-factor continuous Gabor wavelet transform (CMQGWT) and fast spectral coherence (Fast-SC)

respectively. Subsequently

a CNN model with dual input channels was constructed

allowing for the fusion of deep time-frequency features extracted from each channel into a new feature at a feature fusion layer. Finally

the diagnosis results were output using a classifier. Through classification and recognition experiments involving single and compound faults in rolling bearings for high-speed trains

compared with the CNN model with a single input channel

the proposed model demonstrates superior diagnostic accuracy and robustness.

关键词

滚动轴承卷积神经网络特征融合故障诊断高速列车

Keywords

rolling bearingconvolution neural networkfeature fusionfault diagnosishigh-speed train

references

WU Wenyi, YI Cai, BAI Jie, et al. Envelope harmonic noise ratio based adaptive kurtogram and its application in bearing compound fault identification[J]. IEEE sensors journal, 2022, 22(9): 8701-8714.

张西宁, 郭清林, 刘书语. 深度学习技术及其故障诊断应用分析与展望[J]. 西安交通大学学报, 2020, 54(12): 1-13.

ZHANG Xining, GUO Qinglin, LIU Shuyu. Analysis and prospect of deep learning technology and its fault diagnosis application[J]. Journal of Xi'an jiaotong university, 2020, 54(12): 1-13.

雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5): 94-104.

LEI Yaguo, JIA Feng, KONG Detong, et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of mechanical engineering, 2018, 54(5): 94-104.

林诗麒, 陈智丽, 李宇鹏, 等. 基于深度学习和多域决策融合的轴承故障智能诊断技术[J/OL]. 计算机集成制造系统: 1-15. (2022-08-17) [2023-10-22]. http://kns.cnki.net/kcms/detail/11.5946.TP.20220816.1522.004.htmlhttp://kns.cnki.net/kcms/detail/11.5946.TP.20220816.1522.004.html.

LIN Shiqi, CHEN Zhili, LI Yupeng, et al. Intelligent bearing fault diagnosis technology based on deep learning and multi-domain decision fusion[J/OL]. Computer integrated manufacturing systems: 1-15. (2022-08-17) [2023-10-22]. http://kns.cnki.net/kcms/detail/11.5946.TP.20220816.1522.004.htmlhttp://kns.cnki.net/kcms/detail/11.5946.TP.20220816.1522.004.html.

姚德臣, 刘恒畅, 杨建伟, 等. 基于深度学习的城轨列车轴承复合故障诊断研究[J]. 铁道学报, 2021, 43(6): 37-44.

YAO Dechen, LIU Hengchang, YANG Jianwei, et al. Diagnosis of compound faults of bearings of urban rail train based on deep learning model[J]. Journal of the China railway society, 2021, 43(6): 37-44.

唐波, 陈慎慎. 基于深度卷积神经网络的轴承故障诊断方法[J]. 电子测量与仪器学报, 2020, 34(3): 88-93.

TANG Bo, CHEN Shenshen. Method of bearing fault diagnosis based on deep convolutional neural network[J]. Journal of electronic measurement and instrument, 2020, 34(3): 88-93.

杨劼立, 林建辉, 谌亮. 基于MED辅助特征提取CNN模型的列车轴承故障诊断方法[J]. 中国测试, 2020, 46(10): 124-129.

YANG Jieli, LIN Jianhui, CHEN Liang. Fault diagnosis method for train bearings based on MED-assisted feature extraction CNN model[J]. China measurement & test, 2020, 46(10): 124-129.

DING Xiaoxi, HE Qingbo. Energy-fluctuated multiscale feature learning with deep ConvNet for intelligent spindle bearing fault diagnosis[J]. IEEE transactions on instrumentation and measurement, 2017, 66(8): 1926-1935.

SITTLER R W. An optimal data association problem in surveillance theory[J]. IEEE transactions on military electronics, 1964, 8(2): 125-139.

TANG Xianghong, GU Xin, WANG Jiachen, et al. A bearing fault diagnosis method based on feature selection feedback network and improved D-S evidence fusion[J]. IEEE access, 2020, 8: 20523-20536.

HOANG D T, KANG H J. A motor current signal-based bearing fault diagnosis using deep learning and information fusion[J]. IEEE transactions on instrumentation and measurement, 2020, 69(6): 3325-3333.

齐咏生, 白宇, 高胜利, 等. 基于改进的数据融合滚动轴承故障诊断[J]. 铁道学报, 2022, 44(10): 24-32.

QI Yongsheng, BAI Yu, GAO Shengli, et al. Fault diagnosis of rolling bearing based on improved data fusion[J]. Journal of the China railway society, 2022, 44(10): 24-32.

ZHANG Xin, LIU Zhiwen, WANG Jiaxu, et al. Time-frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets[J]. ISA transactions, 2019, 87: 225-234.

唐贵基, 田甜, 庞彬. 基于总变差去噪和快速谱相关的滚动轴承故障诊断[J]. 振动与冲击, 2019, 38(11): 187-193.

TANG Guiji, TIAN Tian, PANG Bin. Rolling bearing fault diagnosis method based on total variation de-noising and fast spectral correlation[J]. Journal of vibration and shock, 2019, 38(11): 187-193.

LECUN Y, BOSER B, DENKER J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural computation, 1989, 1(4): 541-551.

SHAO Haidong, JIANG Hongkai, LIN Ying, et al. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders[J]. Mechanical systems and signal processing, 2018, 102: 278-297.

樊星男, 刘晓娟. 二维卷积神经网络在轴承故障诊断中的应用[J]. 机械设计与研究, 2022, 38(3): 109-113.

FAN Xingnan, LIU Xiaojuan. Application of two-dimensional convolution neural network in bearing fault diagnosis[J]. Machine design & research, 2022, 38(3): 109-113.

李大柱, 牛江, 梁树林, 等. 基于多尺度时频图与卷积神经网络的车轮故障智能诊断[J]. 铁道科学与工程学报, 2023, 20(3): 1032-1043.

LI Dazhu, NIU Jiang, LIANG Shulin, et al. Intelligent wheel fault diagnosis based on multi-scale time-frequency map and convolutional neural network[J]. Journal of railway science and engineering, 2023, 20(3): 1032-1043.

GAO Yangde, KIM C H, KIM J M. A novel hybrid deep learning method for fault diagnosis of rotating machinery based on extended WDCNN and long short-term memory[J]. Sensors, 2021, 21(19): 6614.

SHI Xiaoyu, QIU Gen, YIN Chun, et al. An improved bearing fault diagnosis scheme based on hierarchical fuzzy entropy and alexnet network[J]. IEEE access, 2021, 9: 61710-61720.

HE You, TANG Hesheng, REN Yan, et al. A semi-supervised fault diagnosis method for axial piston pump bearings based on DCGAN[J]. Measurement science and technology, 2021, 32(12): 125104.

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