基于二维图像和CNN-BiGRU网络的滚动轴承故障模式识别

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Abstract In order to ensure the effectiveness of fault diagnosis of rolling bearings, combining the advantages of convolutional neural network in image feature extraction and classification recognition, the one-dimensional vibration signals of rolling bearings were converted into two-dimensional image data by using the gramian angular fields（GAF）, which not only retained the complete information of the signal, but also maintained the signal's time dependence. And on this basis, a diagnosis model based on the convolutional neural network and bidirectional gated recurrent unit（GRU） was proposed. First, the two-dimensional image was taken as the input data of the model, and the spatial features of the image were extracted by the convolutional neural network. Then the time features were filtered by the bidirectional gating loop unit, and finally the pattern recognition was completed by the classifier. Through the diagnosis experiments of different fault degree and different fault position of rolling bearings, the accuracy is 99.63% and 99.28% respectively, and their effects are better than other commonly used algorithms, which proves the feasibility of the proposed method.

Key words： rolling bearing fault diagnosis gramian angular fields（GAF） two-dimensional image convolutional neural network bidirectional gated recurrent unit（GRU）

Received: 20 July 2020 Published: 15 December 2021

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	Articles by authors
	ZHANG Xunjie1
	ZHANG Min1
	2
	LI Xianjun1

Cite this article:

ZHANG Xunjie1,ZHANG Min1,2, et al. Rolling bearing fault mode recognition based on 2D image and CNN-BiGRU[J]. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(23): 194-201.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I23/194

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