基于贝叶斯优化与改进LeNet-5的滚动轴承故障诊断

doi:10.3969/j.issn.1000-1158.2022.07.12

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Abstract Considering that the network structure is difficult to determine, too many training times and too long training time exist in rolling bearing fault diagnosis by convolutional neural network (CNN), a Bayesian optimization algorithm for improving LeNet-5 is designed, and a bearing fault diagnosis model constructed by this algorithm is presented.The learning rate and other hyperparameters in the Bayesian optimization training process are adopted, the original vibration signals of various fault bearings are directly used as the input of the improved LeNet-5 network, batch normalization is adopted for the pooled output and the activation function of the improved pool layer is adopted to prevent over-fitting, and the global average pool layer is used to replace the full connection layer to improve the generalization ability of the improved LeNet-5 network, the fault classification of rolling bearing is realized by softmax classifier.The experimental results show the accuracy of bearing fault diagnosis model constructed by this algorithm training set is 99.94%, the accuracy of verification set is 99.89%, and the accuracy of test set is 99.65%, compared with 1D-CNN and 2D-CNN, bearing fault diagnosis model based on Bayesian optimization and improved LeNet-5 algorithm has higher accuracy, less training times and training time in the fault diagnosis model of rolling bearings.

Key words： metrology rolling bearings fault diagnosis improved LeNet-5 network Bayesian optimization deep learning

Received: 18 August 2021 Published: 18 July 2022

PACS:	TB936
	TB973

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	Articles by authors
	TANG Liang
	FAN Yan-feng
	XU Shi-fei
	CAI Kai-yi

Cite this article:

TANG Liang,FAN Yan-feng,XU Shi-fei, et al. Fault Diagnosis of Rolling Bearing Based on Bayesian Optimization and Improved LeNet-5[J]. Acta Metrologica Sinica, 2022, 43(7): 913-919.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.07.12 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I7/913

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