Abstract
In order to solve the problem that it is difficult to extract slight fault features in the process of fault diagnosis of rolling bearing, this paper proposes a fault diagnosis method based on variational mode decomposition (VMD) and modified convolution neural network (CNN). Firstly, in the process of eigenvalue extraction, VMD decomposition is used to extract more fault feature details of rolling bearing vibration signals; then, dense block and other methods are applied in the network with fewer layers; finally, global average pooling is used instead of full connection layer for the complex calculation of full connection layer. Through the diagnosis experiments of different fault conditions of rolling bearing, it is proved that the proposed method can improve the fault recognition rate and has good feasibility.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant No. 61973116). The reviewers’ insightful comments and valuable suggestions are also greatly appreciated.
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Hou, G., Yao, C., Gong, L., Zhang, J. (2020). Bearing Fault Diagnosis Based on Variational Mode Decomposition and Modified CNN. In: Fei, M., Li, K., Yang, Z., Niu, Q., Li, X. (eds) Recent Featured Applications of Artificial Intelligence Methods. LSMS 2020 and ICSEE 2020 Workshops. LSMS ICSEE 2020 2020. Communications in Computer and Information Science, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-6378-6_16
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DOI: https://doi.org/10.1007/978-981-33-6378-6_16
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