New method for the fault diagnosis of rolling bearings based on a multiscale convolutional neural network

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (18) : 212-220.

XU Zifei1,JIN Jiangtao1,LI Chun1,2

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Abstract

In order to improving the accuracy of rolling bearings fault diagnosis in complex working environments, and considering the multiple time scale characteristic of the measured signals, a new algorithm named multiple time scale characteristic extracted convolutional neural network, (MTSC-CNN) was proposed to develop an end-to-end fault diagnosis system.The proposed MTSC-CNN was used to realize fault identification under 11 working conditions of the tested bearings, including different fault types and damage degrees for verifying the effectiveness of the proposed model.The results show that when only single time scale is considered, the performance of the model is poor due to lack of information.Too larger time scale will lead to over-extraction of information, which will increase the computational time and weaken the diagnostic capability of the model.Compared with the existing methods, the MTSC-CNN model has better performance under variable load and noise conditions.In addition, the results of neural network visualization also show that the features learned at different scales are complementary to improve the robustness of the model.

Key words

fault diagnosis / convolutional neural network(CNN) / bearing / multiscale

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XU Zifei,JIN Jiangtao,LI Chun. New method for the fault diagnosis of rolling bearings based on a multiscale convolutional neural network[J]. Journal of Vibration and Shock, 2021, 40(18): 212-220

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