[1] Wu Jiachen, Hu Jianzhong, Xu Yadong,. Fault diagnosis method of rolling bearing based onthreshold denoising synchrosqueezing transform and CNN [J]. Journal of Southeast University (English Edition), 2020, 36 (1): 32-40. [doi:10.3969/j.issn.1003-7985.2020.01.005]

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Fault diagnosis method of rolling bearing based onthreshold denoising synchrosqueezing transform and CNN()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

36

Issue:

2020 1

Page:

32-40

Research Field:

Mechanical Engineering

Publishing date:

2020-03-20

Info

Title:

Fault diagnosis method of rolling bearing based onthreshold denoising synchrosqueezing transform and CNN

Author(s):

Wu Jiachen;  Hu Jianzhong;  Xu Yadong

School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Keywords:

threshold denoising;  synchrosqueezing transform;  convolutional neural network;  rolling bearing

PACS:

TH133.3;TP18

DOI:

10.3969/j.issn.1003-7985.2020.01.005

Abstract:

The rolling bearing vibration signal is non-stationary and is easily disturbed by background noise, so it is difficult to accurately diagnose bearing faults. A fault diagnosis method of rolling bearing based on the time-frequency threshold denoising synchrosqueezing transform(TDSST)and convolutional neural network(CNN)is proposed. Since the traditional methods of wavelet threshold denoising and wavelet adjacent coefficient denoising are greatly affected by the estimation accuracy of noise variance, a time-frequency denoising method based on the STFT spectral correlation coefficient threshold optimization is adopted, which is combined with a synchrosqueezing transform. The ability of the TDSST to reduce noise and improve time-frequency resolution was verified by simulated impact fault signals of rolling bearings. Finally, the CNN is utilized to diagnose the time-frequency diagrams obtained by the TDSST. The diagnostic results of the rolling bearing experimental data show that the proposed method can effectively improve the accuracy of diagnosis. When the SNR of the bearing signal is larger than 0 dB, the accuracy is over 95%, even when the SNR reduces to -4 dB, the accuracy is still around 80%. Moreover, the standard deviation of multiple test results is small, which means that the method has good robustness.

References:

[1] Li J M, Yao X F, Wang H, et al. Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis[J].Mechanical Systems and Signal Processing, 2019, 126: 568-589. DOI:10.1016/j.ymssp.2019.02.056.
[2] Guo J C, Zhen D, Li H Y, et al. Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method[J].Measurement, 2019, 139: 226-235. DOI:10.1016/j.measurement.2019.02.072.
[3] Yan X A, Liu Y, Jia M P. A feature selection framework-based multiscale morphological analysis algorithm for fault diagnosis of rolling element bearing[J].IEEE Access, 2019, 7: 123436-123452. DOI:10.1109/access.2019.2937751.
[4] Yan X A, Liu Y, Jia M P, et al. A multi-stage hybrid fault diagnosis approach for rolling element bearing under various working conditions[J].IEEE Access, 2019, 7: 138426-138441. DOI:10.1109/access.2019.2937828.
[5] Li H K, Yang R, Ren Y J, et al. Rolling element bearing diagnosis using particle filter and kurtogram[J]. Journal of Mechanical Engineering, 2017, 53(3): 63-72. DOI:10.3901/JME.2017.03.063. (in Chinese)
[6] Zhou F Y, Jin L P, Dong J. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017, 40(6): 1229-1251.(in Chinese)
[7] Eren L, Ince T, Kiranyaz S. A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier[J].Journal of Signal Processing Systems, 2019, 91(2): 179-189. DOI:10.1007/s11265-018-1378-3.
[8] Liu X C, Zhou Q C, Zhao J, et al. Fault diagnosis of rotating machinery under noisy environment conditions based on a 1-D convolutional autoencoder and 1-D convolutional neural network[J].Sensors, 2019, 19(4): 972-1-972-20. DOI:10.3390/s19040972.
[9] Li H, Zhang Q, Qin X R, et al. 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. DOI:10.13465/j.cnki.jvs.2018.19.020. (in Chinese)
[10] Oberlin T, Meignen S, Perrier V. The Fourier-based synchrosqueezing transform[C]//2014 IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP). Florence, Italy, 2014: 315-319. DOI:10.1109/icassp.2014.6853609.
[11] Stankovic L, Djurovic I, Stankovic S, et al. Instantaneous frequency in time-frequency analysis: Enhanced concepts and performance of estimation algorithms[J].Digital Signal Processing, 2014, 35: 1-13. DOI:10.1016/j.dsp.2014.09.008.
[12] Yu G, Wang Z H, Zhao P. Multisynchrosqueezing transform[J].IEEE Transactions on Industrial Electronics, 2019, 66(7): 5441-5455. DOI:10.1109/tie.2018.2868296.
[13] Su W S, Wang F T, Zhu H, et al. Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement[J].Mechanical Systems and Signal Processing, 2010, 24(5): 1458-1472. DOI:10.1016/j.ymssp.2009.11.011.
[14] Cai T T, Silverman B W. Incorporating information on neighboring coefficients into wavelet estimation[J]. Sankhya, 2001, 63(2): 127-148.
[15] Wang S B, Chen X F, Cai G G, et al. Matching demodulation transform and synchrosqueezing in time-frequency analysis[J]. IEEE Transactions on Signal Processing, 2014, 62(1): 69-84. DOI:10.1109/tsp.2013.2276393.
[16] Wang Y H, Xu C, Xu C, et al. Packing convolutional neural networks in the frequency domain[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(10): 2495-2510. DOI:10.1109/tpami.2018.2857824.
[17] Sun J, Cao W, Xu Z, et al. Learning a convolutional neural network for non-uniform motion blur removal[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Boston, MA, USA, 2015: 769-777. DOI:10.1109/cvpr.2015.7298677.
[18] Yuan J H, Han T, Tang J, et al. Intelligent fault diagnosis method for rolling bearings based on wavelet time-frequency diagram and CNN[J]. Machine Design and Research, 2017, 33(2): 93-97.(in Chinese)

Memo

Memo:

Biographies: Wu Jiachen(1995—), male, graduate; Hu Jianzhong(corresponding author), male, doctor, associate professor, hjz@seu.edu.cn.
Citation: Wu Jiachen, Hu Jianzhong, Xu Yadong. Fault diagnosis method of rolling bearing based on threshold denoising synchrosqueezing transform and CNN[J].Journal of Southeast University(English Edition), 2020, 36(1):32-40.DOI:10.3969/j.issn.1003-7985.2020.01.005.

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