基于注意力与多尺度卷积神经网络的电机轴承故障诊断

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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

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	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.

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

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