An improved fault diagnosis method of rolling bearings based on LeNet-5

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (12) : 55-61.

WU Chenfang1，YANG Shixi1，HUANG Haizhou2,GU Xiwen1，SUI Yongfeng3

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Abstract

This paper proposed a convolutional neural network fault diagnosis method based on improved LeNet-5, aiming at the incompleteness of rolling bearing fault samples.This method takes the original time-domain vibration signals of rolling bearing containing multiple speeds as the input of the model in the form of two-dimensional grayscale images.The input size is determined according to the signal characteristics, and features are extracted adaptively through convolution operations.This method introduces a batch normalization operation to improve the model and uses the softmax classifier to implement fault classification and recognition.Finally, the t-distribution neighborhood embedding algorithm (t-SNE) is used to objectively demonstrate the feature extraction effect of the method.The rationality and effectiveness of the improved model were verified by the multi-fault experimental analysis of rolling bearings.Experimental results show that by training the rolling bearing fault data at four speeds can learn the common characteristics of bearing fault samples with limited speed, accurate classification of rolling bearing faults can be achieved.And the fault data at other speeds are also valid, which broadens the speed generalization ability of rolling bearing fault diagnosis.The BP neural network (BPNN) and support vector machine (SVM) algorithms were compared with the method proposed in this paper, which proves that the method has good robustness and generalization ability.This work can provide reference and reference for ensuring the reliability of rolling bearings and the safe operation of equipment.

Key words

fault diagnosis / CNN / LeNet-5 / speed generalization / rolling bearing

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WU Chenfang1，YANG Shixi1，HUANG Haizhou2,GU Xiwen1，SUI Yongfeng3. An improved fault diagnosis method of rolling bearings based on LeNet-5[J]. Journal of Vibration and Shock, 2021, 40(12): 55-61

References

［1］祝燮权.实用滚动轴承手册［M］.上海:上海科学技术出版社, 2002.

［2］肖述兵.滚动轴承振动故障诊断实践［J］.轴承, 2006(3): 31-33.
XIAO Shubing.Fault diagnosis of rolling bearing vibration［J］.Bearing, 2006(3): 31-33.

［3］王国彪, 何正嘉, 陈雪峰, 等.机械故障诊断基础研究“何去何从”［J］.机械工程学报, 2013,49(1): 63-72.
WANG Guobiao, HE Zhengjia, CHEN Xuefeng, et al.Basic research on machinery fault diagnosis—what is the prescription［J］.Journal of Mechanical Engineering, 2013,49(1): 63-72.

［4］钟秉林, 黄仁.机械故障诊断学［M］.北京:机械工业出版社, 2007.

［5］XIONG Xin, YANG Shixi, GAN Chunbiao.A new procedure for extracting fault feature of multi-frequency signal from rotating machinery［J］.Mechanical Systems and Signal Processing, 2012,32: 306-319.

［6］JIN Xiaohang, SUN Yi, SHAN Jihong, et al.Fault diagnosis and prognosis for wind turbines: An overview［J］.Chinese Journal of Scientific Instrument, 2017,38(5): 1041-1054.

［7］RANDALL R B, ANTONI J.Rolling element bearing diagnostics—A tutorial［J］.Mechanical Systems and Signal Processing, 2011,25(2): 485-520.

［8］GUO Lili, DING Shifei.Research progress on deep learning［J］.Computer Science, 2015,42(5): 28-33.

［9］DIAO Wenhui, SUN Xian, ZHENG Xinwei, et al.Efficient saliency-based object detection in remote sensing images using deep belief networks［J］.IEEE Geoscience and Remote Sensing Letters, 2016,13(2): 137-141.

［10］ZENG Nianyin, ZHANG Hong, SONG Baoye, et al.Facial expression recognition via learning deep sparse autoencoders［J］.Neurocomputing, 2018,273: 643-649.

［11］LECUN Y, BENGIO Y, HINTON G.Deep learning［J］.Nature, 2015,521(7553): 436-444.

［12］FUKUSHIMA K.Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position［J］.Biological Cybernetics, 1980,36(4): 193-202.

［13］LECUN Y, BOTTOU L, BENGIO Y, et al.Gradient-based learning applied to document recognition［J］.Proceedings of the IEEE, 1998,86(11): 2278-2324.

［14］KRIZHEVSKY A, SUTSKEVER I, HINTON G E.Imagenet classification with deep convolutional neural networks［C］//Advances in neural information processing systems, 2012.

［15］HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al.Deep residual learning for image recognition［C］//Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.

［16］HAN Tao, YUAN Jianhu, TANG Jian, et al.An approach of intelligent compound fault diagnosis of rolling bearing based on MWT and CNN［J］.Journal of Mechanical Transmission, 2016(12): 31.

［17］LI Xiaolei, DING Pengli, SHI Xiaobing.Research on bearing fault detection based on convolution neural network［C］//2017 Chinese Automation Congress (CAC).IEEE, 2017.

［18］JANSSENS O, SLAVKOVIKJ V, VERVISCH B, et al.Convolutional neural network based fault detection for rotating machinery［J］.Journal of Sound and Vibration, 2016,377: 331-345.

［19］WANG Tianyang, HAN Qinkai, CHU Fulei, et al.Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: a review［J］.Mechanical Systems and Signal Processing, 2019,126: 662-685.

［20］张青青.基于改进AlexNet的滚动轴承变工况故障诊断研究［D］.兰州：兰州理工大学, 2019.

［21］胡晓依, 荆云建, 宋志坤, 等.基于CNN-SVM的深度卷积神经网络轴承故障识别研究［J］.振动与冲击, 2019,38(18): 173-178.
HU Xiaoyi, JING Yunjian, SONG Zhikun, et al.Bearing fault identification by using deep convolution neural networks based on CNN-SVM［J］.Journal of Vibration and Shock, 2019,38(18): 173-178.

［22］BOUVRIE J.Notes on convolutional neural networks［R］.USA: MIT, 2006.

［23］IOFFE S, SZEGEDY C.Batch normalization: accelerating deep network training by reducing internal covariate shift［C］// International Conference on International Conference on Machine Learning.JMLR.org, 2015.

［24］MAATEN L, HINTON G.Visualizing data using t-SNE［J］.Journal of Machine Learning Research, 2008,9: 2579-2605.

［25］ALI J B, FNAIECH N, SAIDI L, et al.Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals［J］.Applied Acoustics, 2015,89: 16-27.

［26］HE Jun, YANG Shixi, GAN Chunbiao.Unsupervised fault diagnosis of a gear transmission chain using a deep belief network［J］.Sensors, 2017,17(7): 1564.