基于卷积神经网络图像分类的轴承故障模式识别

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (4) : 165-171.

论文

张安安1，黄晋英1，冀树伟2，李东1

作者信息 +

Bearing fault pattern recognition based on image classification with CNN

ZHANG An’an1,HUANG Jinying1,JI Shuwei2,LI Dong1

Author information +

文章历史 +

摘要

针对传统的基于数据驱动的机械故障模式识别方法中需要人工构造算法提取特征以及人工构造特征提取算法繁琐的问题，结合卷积神经网络(CNN)在图像特征自动提取与图像分类识别中的广泛应用，提出了一种基于CNN图像分类的轴承故障模式识别方法。首先，利用集合经验模态分解(EEMD)方法对轴承振动信号进行自适应分解并用相关系数对得到的本征模函数分量进行筛选。其次，对筛选得到的本征模函数分量进行伪魏格纳-威利时频分析（PWVD）计算得到信号的时频分布图，并对时频图进行预处理。最后，将轴承15种不同工况预处理后的时频图利用CNN进行特征提取与分类识别。将该方法与同类方法进行了对比，分类正确率提高了4.26%。

Abstract

Conventional machinery fault pattern recognition methods based on data-driven need to construct algorithms manually to extract characteristics. Manually constructed feature extraction algorithms are complicated. In order to solve the problems , a bearing fault pattern recognition method based on image classification with CNN was proposed, with the consideration of the wide application of CNN in image feature automatic extraction and classification. Firstly, with the EEMD method, bearing vibration signals were decomposed and a finite number of intrinsic mode functions(IMF) were selected according to the criterion of correlation coefficient. Secondly, with the PWVD method, time-frequency diagrams were obtained from selected IMFs and preprocessing was carried on time-frequency diagrams. Lastly, fifteen kinds of preprocessed bearing time-frequency diagrams served as import of CNN for feature extraction and classification. Comparison was made between this methodology and another similar method, and the classification accuracy improved by 4.26%.

导出引用

张安安1，黄晋英1，冀树伟2，李东1. 基于卷积神经网络图像分类的轴承故障模式识别[J]. 振动与冲击, 2020, 39(4): 165-171

ZHANG An’an1,HUANG Jinying1,JI Shuwei2,LI Dong1. Bearing fault pattern recognition based on image classification with CNN[J]. Journal of Vibration and Shock, 2020, 39(4): 165-171

参考文献

[1] YAN X A, JIA M P. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing [J]. Neurocomputing, 2018, 313:47-64.
[2] LEI Y G, JIA F, LIN J, et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data [J]. IEEE Transactions on Industrial Electronics, 2016.
[3] 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.
[4] JIA F, LEI Y G, LIN J, et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data [J]. Mechanical Systems and Signal Processing, 2016,72-73:303-315.
[5] JING L Y, ZHAO M, LI P, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox [J]. Measurement, 2017,111:1-10.
[6] 蔡艳平，李艾华，王涛，等．基于时频谱图与图像分割的柴油机故障诊断[J]．内燃机学报，2011，29(2):181-186．
CAI Yanping，LI Aihua，WANG Tao，et al．IC diesel engine fault diagnosis based on time-frequency spectrum image and image segmentation[J]．Transactions of CSICE，2011，29( 2) : 181-186．
[7]   沈虹，赵红东，梅检民，等．基于高阶累积量图像特征的柴油机故障诊断研究[J]．振动与冲击，2015，34(11):133-138．
SHEN Hong，ZHAO Hongdong，MEI Jianmin ，et   al ．
Diesel engine fault diagnosis based on high-order cumulant image features [J]．Journal of Vibration and Shock，2015 ，34( 11) :133-138．
[8] 牟伟杰, 石林锁, 蔡艳平,等. 基于 KVMD-PWVD与 LNMF的内燃机振动谱图像识别诊断方法[J]. 振动与冲击, 2017, 36(2):45-51.
MU Weijie，SHI Linsuo，CAI Yanping，et al．IC engine fault diagnosis method based on KVMD-PWVD and LNMF [J]．Journal of Vibration and Shock，2017，36( 2) : 45-51．
[9] 汤鹏杰, 王瀚漓, 左凌轩. 并行交叉的深度卷积神经网络模型[J]. 中国图象图形学报, 2016,21(3):339-347.
TANG Pengjie，WANG Hanli，ZUO Lingxuan. Parallel cross deep convolution neural networks model [J]. Journal of Image and Graphics,2016, 21(3):339-347.
[10] WU Z H，HUANG N E. Ensemble empirical mode decomposition: a noise assisted data analysis method [J]．Advances in Adaptive Data Analysis, 2009,1(1) :1-41．
[11] 胡爱军, 马万里, 唐贵基. 基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J]. 中国电机工程学报, 2012,32(11):106-111.
HU Aijun，MA Wanli，TANG Guiji. Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and kurtosis criterion[J]. Proceedings of the CSEE, 2012,32(11):106-111.
[12] 刘永强, 李翠省, 廖英英. 基于EEMD 和自相关函数峰态系数的轴承故障诊断方法[J]. 振动与冲击,2017,36(2):111-116.
LIU Yongqiang，LI Cuixing，LIAO Yingying. Fault diagnosis method for rolling bearings based on EEMD and autocorrelation function kurtosis [J]. Journal of Vibration and Shock,2017,36(2):111-116.
[13] 张玲玲，肖静. 基于MATLAB的机械故障诊断技术案例教程[M]. 北京：高等教育出版社，2016:151-180.
   ZHANG Lingling，XIAO Jing . Case study of mechanical fault diagnosis technology based on MATLAB[M]. Beijing: Higher Education Press, 2016:151-180.
[14] 周飞燕,金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017,40(6):1229-1251.
ZHOU Feiyan，JIN Linpeng，DONG Jun. Review of convolutional neural network [J]. Chinese Journal of Computers, 2017,40(6):1229-1251.
[15] 任浩, 屈剑锋, 柴毅,等. 深度学习在故障诊断领域中的研究现状与挑战[J]. 控制与决策, 2017,32(8):1345-1358.
REN Hao，QU Jianfeng，CHAI Yi, et al. Deep learning for fault diagnosis: the state of the art and challenge[J]. Control and Decision, 2017,32(8):1345-1358.
[16] 吴虎胜，吕建新，来凌红，等. 基于EMD-SVD模型和SVM滚动轴承故障模式识别[J]. 噪声与振动控制，2011，31(2)：89-93.
WU Husheng，LV Jianxin，LAI Linghong，et al. Fault pattern recognition of rolling bearing based on EMD-SVD model and SVM [J]. Noise and Vibration Control,2011, 31(2):89-93.
[17] 张慧娜, 李裕梅, 傅莺莺. 基于Haar-CNN模型的自然场景图像分类的研究[J]. 四川师范大学学报(自然科学版), 2017,40(1):119-126.
ZHANG Huina，LI Yumei，FU Yingying. Research on natural scene image classification based on Haar-CNN model [J]. Journal of Sichuan Normal University(Natural Science), 2017,40(1):119-126.
[18] SIMONYAN K，ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [J]. Computer Science，2014.
[19] SZEGEDY C，LIU W，JIA Y Q，et al. Going deeper with convolutions [C] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015：1-9.
[20] HINTON G E，SRIVASTAVA N，KRIZHEVSKY A，et al. Improving neural networks by preventing co-adaptation of feature detectors [J]. Computer Science，2012，3(4)：212-223.
[21] 孙文珺, 邵思羽, 严如强. 基于稀疏自动编码深度神经网络的感应电动机故障诊断[J]. 机械工程学报, 2016, 52(9):65-71.
SUN Wenjun，SHAO Siyu，YAN Ruqiang. Induction motor fault diagnosis based on deep neural network of sparse auto-encoder [J]. Journal of Mechanical Engineering,2016, 52(9)
:65-71.
[22] WAN L，ZEILER M， ZHANG S X，et al. Regularization of neural networks using DropConnect [C] // International Conference on Machine Learning, 2013:1058-1066.
[23] Case Western Reserve University, Bearing Data Center [OL]. Available:https://csegroups.case.edu/bearingdatacenter/pages/download-data-file.
[24] HAN Y, TANG B P, DENG L. Multi-level wavelet packet fusion in dynamic ensemble convolutional neural network for fault diagnosis[J]. Measurement, 2018,127:246-255.
[25] JIAO J Y，ZHAO M，LIN J，et al. A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes [J]. Knowledge-Based Systems,2018.
[26] 邹国锋, 傅桂霞, 王科俊,等. 自适应深度卷积神经网络模型构建方法[J]. 北京邮电大学学报, 2017, 40(4):98-103.
ZOU Guofeng，FU Guixia，WANG Kejun，et al. Construction method of adaptive deep convolutional neural network model [J]. Journal of Beijing University of Posts and Telecommunications,2017, 40(4):98-103.
[27] 王丽华, 谢阳阳, 周子贤,等. 基于卷积神经网络的异步电机故障诊断[J]. 振动、测试与诊断,2017,37(6):1208-1215.
WANG Lihua，XIE Yangyang，ZHOU Zixian，et al. Motor fault diagnosis based on convolutional neural networks[J]. Journal of Vibration,Measurement & Diagnosis,2017,37 (6):1208-1215.