Fault Diagnosis Method of Motor Bearing Based on Improved GAN Algorithm

doi:10.12068/j.issn.1005-3026.2019.12.002

Abstract

Abstract: A fault diagnosis method was proposed for rolling bearing based on improved generation adversarial networks(GAN). Taking the vibration signal as the main basis， combined with the ability of continuous wavelet transform to process non-stationary signals and the functions of semi-supervised generation adversarial networks (SSGAN) processing and image recognition for fault diagnosis， the condition model was introduced based on semi-supervised generation adversarial networks， and the loss function was optimized to guide the generator and discriminator. For the first time， the improved GAN algorithm was applied to the field of fault diagnosis and its generation model and semi-supervised learning ability were used to solve the problem of sample data shortage and sample labeling. Experimental results showed that the fault diagnosis method combining continuous wavelet transform and improved GAN can achieve higher accuracy than that of the other mainstream diagnostic methods.

Key words: bearing, continuous wavelet transform, time-frequency representations, semi-supervised learning, generation adversarial networks, fault diagnosis

CLC Number:

TP18

XU Lin， ZHENG Xiao-tong， FU Bo， TIAN Ge. Fault Diagnosis Method of Motor Bearing Based on Improved GAN Algorithm[J]. Journal of Northeastern University Natural Science, 2019, 40(12): 1679-1684.

References

[1]Yang Z，Gao L.Wavelet analysis and fault feature extraction of rolling bearing［M］.London:Springer， 2014.
[2]Jawade A，Paraskar S，Jadhav S.Artificial neural network based induction motor fault classifier using continuous wavelet transform［J］.Systems Science & Control Engineering，2014，2(1):684-690.
[3]袁建虎，韩涛，唐建，等.基于小波时频图和CNN的滚动轴承智能故障诊断方法［J］.机械设计与研究，2017，33(2):93-97.(Yuan Jian-hu，Han Tao，Tang Jian， et al.An approach to intelligent fault diagnosis of rolling bearing using wavelet time-frequency representations and CNN［J］.Machine Design and Research，2017，33(2):93-97.)
[4]徐久强，洪丽萍，朱宏博，等.一种用于肺结节恶性度分类的生成对抗网络［J］.东北大学学报(自然科学版)，2018，39(11):1556-1562.(Xu Jiu-qiang，Hong Li-ping，Zhu Hong-bo，et al.Generative adversarial networks for the classification of lung nodules malignant［J］.Journal of Northeastern University(Natural Science)，2018，39(11):1556-1562.)
[5]Goodfellow I，Pouget-Abadie J，Mirza M，et al.Generative adversarial nets［C］//Proceedings of the 27th Conference on Advances in Neural Information Processing Systems.Montreal:Curran Associates，Inc，2014:2672-2680.
[6]Salimans T，Goodfellow I，Zaremba W.Improved techniques for training GANs［EB/OL］.(2016-06-10).https://arXiv.org/abs/1606.03498.
[7]Yu L T，Zhang W N，Wang J，et al.SeqGAN:sequence generative adversarial nets with policy gradient［C］//Proceedings of the 31st AAAI Conference on Artificial Intelligence.San Francisco，2017:2852-2858.
[8]Ahsan U，Sun C，Essa I.DiscrimNet:Semi-supervised action recognition from videos using generative adversarial networks［EB/OL］.(2018-01-22).https://arXiv.org/abs/1801.07230.
[9]Levie R，Sochen N.A wavelet Plancherel theory with application to sparse continuous wavelet transform［EB/OL］.(2018-08-17).https://arXiv.org/abs/1712.02770.
[10]Lolparo K A.Bearing vibration data set［D］.Cleveland:Case Western Reserve University，2003.
[11]Lyu L，Yuan R，Song G B.Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing［J］.Mechanical Systems and Signal Processing，2016，81:219-234.
[12]Chen Z Q，Li C，Sanchenz R.Gearbox fault identification and classification with convolutional neural networks［J］.Shock and Vibration，2015(2):1-10.
[13]李辉，郑海起，唐立伟.声测法和经验模态分解在轴承故障诊断中的应用［J］.中国电机工程学报，2006，26(15):124-128.(Li Hui，Zheng Hai-qi，Tang Li-wei.Application acoustic emission and empirical mode decomposition to faults diagnosis of bearing［J］.Proceeding of the CSEE，2006，26(15):124-128.)
[14]曲建岭，余路，袁涛，等.基于一维卷积神经网络的滚动轴承自适应诊断算法［J］.仪器仪表学报，2018，39(2):134-143.(Qu Jian-ling，Yu Lu，Yuan Tao，et al.Adaptive fault diagnosis algorithm for rolling bearings based on one-dimensional convolutional neural network［J］.Chinese Journal of Scientific Instrument，2018，39(2):134-143.)
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new interval particle swarm optimization algorithm ［J］，Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)