Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network

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Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (19) : 124-131.

LI Heng, ZHANG Qin, QIN Xianrong, SUN Yuantao

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Abstract

Aiming at fault vibration signals of rolling bearings with features of stronger non-stationarity and easy to be disturbed by strong background noise, a fault diagnosis method based on the short-time Fourier transform (STFT) and the convolution neural network (CNN) was proposed to realize the end-to-end fault pattern recognition.Firstly, STFT was performed for vibration signals of rolling bearings to get their time-frequency spectrum samples divided into a training set and a test one.Then, the training set was input into CNN to do learning and update parameters of CNN.Finally, the CNN model with updated parameters was applied in the test set to output the fault recognition results.Through simulation tests of rolling bearing faults, the feasibility and effectiveness of the proposed method were verified.The results indicated that the proposed method has higher recognition accuracies for different types of faults; the robustness of this method can be improved with increase in amount and type of fault data; it is a fault diagnosis method suitable for dealing with big data.

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rolling bearing / fault diagnosis / STFT / CNN

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LI Heng, ZHANG Qin, QIN Xianrong, SUN Yuantao. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network[J]. Journal of Vibration and Shock, 2018, 37(19): 124-131

References

[1] B Buxton D G C D. Big data: science in the petabyte era[J]. Nature. 2008, 455(7209): 8-9.
[2] 张小龙，张氢，秦仙蓉，等. 基于ITD-形态滤波和Teager能量谱的轴承故障诊断[J]. 仪器仪表学报. 2016(04): 788-795.
ZHANG Xiaolong, ZHANG Qin, QIN Xianrong, et al. Fault diagnosis method for rolling bearing based on ITD-morphological filter and Teager energy spectrum [J]. Journal of Vibration and Shock, 2014, 2016(04): 788-795.
[3] 钱林，康敏，傅秀清，等. 基于VMD的自适应形态学在轴承故障诊断中的应用[J]. 振动与冲击. 2017(03): 227-233.
QIAN Lin, KANG Min, FU Xiuqing, et al. Application of adaptivemorphology in bearing fault diagnosis based on VMD [J]. Journal of Vibration and Shock, 2017(03): 227-233.
[4] Unal M, Onat M, Demetgul M, et al. Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network[J]. Measurement. 2014, 58: 187-196.
[5] Tamilselvan P, Wang P. Failure diagnosis using deep belief learning based health state classification[J]. Reliability Engineering & System Safety. 2013, 115: 124-135.
[6] 魏东，龚庆武，来文青，等. 基于卷积神经网络的输电线路区内外故障判断及故障选相方法研究[J]. 中国电机工程学报. 2016, 36(S1): 21-28.
WEI Dong, GONG Qingwu, LAI Wenqing, et al. Research on Internal and External Fault Diagnosis and Fault-selection of Transmission Line Based on Convolutional Neural Network. Process of the CSEE, 2016, 36(S1): 21-28.
[7] Lu C, Wang Z, Zhou B. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification [J]. Advanced Engineering Informatics. 2017, 32: 139-151.
[8] 朱军涛，廖红建，谢勇勇，等. 采用短时傅里叶变换的铁路车载探地雷达数据解译方法[J]. 西安交通大学学报. 2012(07): 108-114.
ZHU Juntao, LIAO Hongjian, XIE Yongyong, et al. Data Interpretation of Ground-Penetrating Radar (GPR) via Short-Time Fourier Transform for Railway Track Detection [J]. Journal of Xi’an Jiaotong University, 2012(07): 108-114.
[9] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097-1105.
[10] Lecun Y, Boser B, Denker J S, et al. Backpropagation Applied to Handwritten Zip Code Recognition[J]. Neural Computation. 1989, 4(1): 541-551.
[11] Bouvrie J. Notes on Convolutional Neural Networks[J]. Neural Nets. 2006.
[12] Glorot X, Bordes A, Bengio Y. Deep Sparse Rectifier Neural Networks[J]. Journal of Machine Learning Research. 2011, 15.
[13] Gu J, Wang Z, Kuen J, et al. Recent Advances in Convolutional Neural Networks [J]. Computer Science. 2017