Fault Diagnosis of Rolling Bearing Based on S-Transform and Convolutional Neural Network

Qingrong Wang; Lei Yang; Songsong Wang

doi:10.3788/LOP202158.2207001

[1] Hu Q Z, Zhang S L, Yang S. Variable condition bearing fault diagnosis based on time-domain and artificial intelligence[J]. Applied Mechanics and Materials, 203, 329-333(2012).

[2] Chen Y, An W Y, Liu H L et al. Detection method using FBG sensing signal to diagnose rolling bearing fault[J]. Chinese Journal of Lasers, 47, 1104004(2020).

[3] Chen Y, Chen Y W, Liu Z Q et al. A gear fault detection method based on a fiber Bragg grating senson[J]. Chinese Journal of Lasers, 47, 0304007(2020).

[4] Wu T, Yuan S C, Meng X et al. Vibrating diagnosis of rolling bearings based on self-organizing feature map neural network[J]. Machinery Design & Manufacture, 198-200(2010).

[5] Chen Z, Hu Y Q, Tian S Q et al. Non-stationary signal combined analysis based fault diagnosis method[J]. Journal on Communications, 41, 187-195(2020).

[6] Kankar P K, Sharma S C, Harsha S P. Rolling element bearing fault diagnosis using wavelet transform[J]. Neurocomputing, 74, 1638-1645(2011).

[7] Stockwell R G, Mansinha L, Lowe R P. Localization of the complex spectrum: the S transform[J]. IEEE Transactions on Signal Processing, 44, 998-1001(1996).

[8] Liu D, Li Y C. Quality assessment of remote sensing images based on deep learning and human visual system[J]. Laser & Optoelectronics Progress, 56, 061101(2019).

[9] Wu C Z, Jiang P C, Feng F Z et al. Faults diagnosis method for gearboxes based on a 1-D convolutional neural network[J]. Journal of Vibration and Shock, 37, 51-56(2018).

[10] Chen R X, Huang X, Yang L X et al. Rolling bearing fault identification based on convolution neural network and discrete wavelet transform[J]. Journal of Vibration Engineering, 31, 883-891(2018).

[11] Yang T. Study on dynamic characteristics of new marine RV gearbox[D], 20-28(2016).

[12] Chen W, Chen J X, Jiang Y Q et al. Fault identification of rolling bearing based on RS-LSTM[J]. China Sciencepaper, 13, 1134-1141(2018).

[13] 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, 37, 124-131(2018).

[14] Shi P M, Su G H, Yin X D. Research on rolling bearing fault diagnosis method based on combination of S transform and deep learning[J]. Modern Manufacturing Engineering, 125-130(2019).

[15] Tang Q, Wang Y N, Guo S Y et al. Power quality disturbance detection based on S-transform and PNN[J]. Chinese Journal of Scientific Instrument, 30, 1668-1673(2009).

[16] Liu H Y, Tian G, Shi Z J. The comparison of time-frequency analysis methods and their applications[J]. Computerized Tomography Theory and Applications, 24, 199-208(2015).

[17] Liu J W, Liu Y, Luo X L. Research and development on deep learning[J]. Application Research of Computers, 31, 1921-1930, 1942(2014).

[18] He Z C, Zhao L Z, Chen C. Convolution neural network with multi-resolution feature fusion for facial expression recognition[J]. Laser & Optoelectronics Progress, 55, 071503(2018).

[19] Haykin S, Kosko B. Gradient-based learning applied to document recognition[M], 306-351(2001).

[20] Wang J X, Lei Z C. A convolutional neural network based on feature fusion for face recognition[J]. Laser & Optoelectronics Progress, 57, 101508(2020).

[21] Guo Y F, Quan W M, Wang W Y et al. Crack diagnosis method of wind turbine blade based on convolution neural network with 3D vibration information fusion[J]. Acta Optica Sinica, 40, 2212004(2020).

[22] Zhang W F, Zhou J. Fault diagnosis method of rolling bearing based on Dropout-CNN[J]. Light Industry Machinery, 37, 62-67(2019).

[23] Meng Z Q, Dong S J, Pan X J et al. Fault diagnosis of bolling bearing based on improved convolutional neural network[J]. Modular Machine Tool & Automatic Manufacturing Technique, 79-83(2020).