基于注意力与多尺度卷积神经网络的电机轴承故障诊断

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (19116 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In this paper, an improved end-to-end convolutional neural network fault diagnosis method is proposed to deal with the problem that motor fault diagnosis needs complex feature extraction process and the diagnostic accuracy is low under the variable working conditions. In this method, the fully convolutional networks was used to prevent the feature loss caused by pooling layer. At the same time, multi-scale fault features were extracted by using convolution neural network of different kernel sizes, so that the method can obtain more abundant and complementary fault feature. Then the attention mechanism was introduced to further screen the obtained fault features, highlighting the key fault features and suppressing the unimportant fault features. Finally, a series of experiments were carried out on the fault data of motor bearing. The experiment results show that the proposed method has strong fault discriminative ability and domain adaptive ability.

Key words： convolutional neural network (CNN) intelligent diagnosis attention mechanism multi-scale feature extraction artificial intelligence

Received: 21 April 2020

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Tang Si
	Chen Xinchu
	Zheng Song

Cite this article:

Tang Si,Chen Xinchu,Zheng Song. Fault diagnosis method of motor bearing based on attention and multi-scale convolution neural network[J]. Electrical Engineering, 2020, 21(11): 32-38.

URL:

https://dqjs.cesmedia.cn/EN/Y2020/V21/I11/32

[1] 安国庆,秦程,郭立炜,等.峭度滤波器用于电机轴承早期故障特征提取[J].电机与控制学报,2014,18(6):55-60.
[2] 陈珊珊,杨耿杰.水电机组振动故障诊断方法综述[J].电气技术,2019,20(6):1-5.
[3] Liu T,Chen J,Dong G M.Rolling element bearing fault diagnosis based on frequency band entropy[J].Journal of Vibration and Shock,2014,33(1):77-80.
[4] 俞啸.数据驱动的滚动轴承故障特征分析与诊断方法研究[D].北京:中国矿业大学,2017.
[5] 邓飞跃,强亚文,杨绍普,等.一种自适应频率窗经验小波变换的滚动轴承故障诊断方法[J].西安交通大学学报,2018,52(8):22-29.
[6] Soualhi A,Clerc G,Razik H,et al.Fault detection and diagnosis of induction motors based on hidden Markov model[C]//2012 International Conference on Electrical Machines.IEEE,2012:1693-1699.
[7] Liao Y,Zhang L,Li W.Regrouping particle swarm optimization based variable neural network for gear-box fault diagnosis[J].Journal of Intelligent & Fuzzy Systems,2018,34(6):3671-3680.
[8] Palácios R C,Da Silva I N,Goedtel A,et al.Diagnosis of stator faults severity in induction motors using two intelligent approaches[J].IEEE Transactions on Indu-strial Informatics,2017,13(4):1681-1691.
[9] 黄丽梅,张旗.基于数据驱动的变换器故障诊断综述[J].电气技术,2019,20(2):1-6,27.
[10] Sun C,Ma M,Zhao Z,et al.Sparse deep stacking network for fault diagnosis of motor[J].IEEE Transa-ctions on Industrial Informatics,2018,14(7):3261-3270.
[11] Zhang W,Peng G,Li C,et al.A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals[J].Sensors,2017,17(2):425.
[12] 肖雄,王健翔,张勇军,等.一种用于轴承故障诊断的二维卷积神经网络优化方法[J].中国电机工程学报,2019,39(15):4558-4568.
[13] Lei J,Liu C,Jiang D.Fault diagnosis of wind turbine based on long short-term memory networks[J].Renewable Energy,2019,133:422-432.
[14] He K,Zhang X,Ren S,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni-tion,2016:770-778.
[15] Zhuang Z,Lv H,Xu J,et al.A deep learning method for bearing fault diagnosis through stacked residual dilated convolutions[J].Applied Sciences,2019,9(9):1823.
[16] Zhang W,Li C,Peng G,et al.A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J].Mechanical Systems and Signal Processing,2018,100:439-453.