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Rolling bearing fault diagnosis under different working conditions based on MRDA

Published:2023-04-20 author:ZHU Ji-yang, SUN Hu-er, ZHANG Tian-yuan, et al. Browse: 1240 Check PDF documents

Rolling bearing fault diagnosis under different working

conditions based on MRDA

ZHU Ji-yang, SUN Hu-er, ZHANG Tian-yuan, ZHAO Yang, BAI Xiao-yi

(School of Mechanical Engineering, The North University of China, Taiyuan 030051, China)

Abstract: In fault diagnosis of rolling bearing under different working conditions, abundantly labeled data was often difficult or even impossible to obtain. Therefore, using the original vibration signal as the input of the neural network, through the multiple representation dynamic adaptive (MRDA) algorithm, the multi-representation aligns the transferable features, and the adaptive dynamic measures the relative importance of the marginal distribution and the conditional distribution, a new deep transfer model was constructed, namely the one-dimensional multi-representation hole dynamic adaptive transfer network (1D MRDDATN). Firstly, transfer learning data distribution was analyzed, and DDA was theoretically deduced. Then, on the basis of one-dimensional dilated convolution, a one-dimensional multi-representation dilated convolution neural network (1D MRDCNN) was created, and the MRDA algorithm and multi-representation dynamic adaptive structure (MRDAM) were proposed to construct a one-dimensional multi-representation dilated dynamic adaptive transfer network (1D MRDDATN). Finally, the rolling bearing data set of CWRU was verified by experiments. The research results show that compared with the traditional deep transfer learning methods, the average diagnostic accuracy of the proposed method is improved and reaches more than 98%. MRDA completes cross-domain classification tasks under different working conditions through multi-representation alignment, and adaptively captures information from different aspects, which achieves better performance.

Key words: different working conditions; one-dimensional multi-representation hole dynamic adaptive transfer network(1D MRDDATN); fault sample; deep transfer learning; multi-representation dynamic adaptive(MRDA) algorithm; neural network; one-dimensional multi-representation dilated convolution neural network (1D MRDCNN)

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