Fault diagnosis of cylindrical roller bearing cage based on 1D convolution neural network
ZHENG Yizhen1, NIU Linkai1,2, XIONG Xiaoyan1,2, QI Hongwei1, MA Xiaoxiong1
1. School of Mechanical and Transportation Engineering, Taiyuan University of Technology, Taiyuan 030024, China;
2. Key Laboratory of New Sensors and Intelligent Control Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China
Abstract：Here, to solve existing problems of instability, non-impact characteristics and difficult to obtain fault characteristics in rolling bearing cage fault vibration signals, an adaptive convolution neural network fault diagnosis model based on "end-to-end" recognition was proposed. Firstly, vibration signals under different cage fault conditions were segmented by overlapping samples according to a certain proportion for data enhancement, and samples were preprocessed by subsection standardization to construct training and test sets. Then, the convolution neural network was used to realize adaptive feature extraction and feature dimensionality reduction of vibration signals. Finally, the global average pooling was used to replace the full connection operation used in the classical framework at the output end, reduce amounts of training model parameters and process calculation, and avoid over-fitting. The diagnosis results were output through Softmax classification. The test results showed that the algorithm can reach more than 99% fault recognition rate, maintain good generalization performance and robustness under different loads and rotating speeds, and be effectively applied in fault diagnosis of bearing cage.
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