Deep learning based on rolling bearing fault diagnosis method


  • Nianyun Liu
  • Weiwei Song



Deep learning; BP neural networks; feature extraction; bearing fault diagnosis.


With the continuous development of industrial automation, rolling bearings play a crucial role in many fields as key mechanical components, and the fault diagnosis of rolling bearings have great significance. This paper discusses a deep learning based rolling bearing fault diagnosis method, aiming to improve the accuracy and efficiency of fault detection. Firstly, the vibration signals of rolling bearings are pre-processed to extract the feature information that helps fault diagnosis. Then, the features were automatically learned and classified by using BP neural network. Finally, the effectiveness and robustness of the method were verified through experiments. Compared with the traditional fault diagnosis method, the deep learning-based rolling bearing fault diagnosis method has higher accuracy and practicality, which provides strong support for the fault detection and preventive maintenance of rolling bearings.


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How to Cite

“Deep learning based on rolling bearing fault diagnosis method” (2023) Transactions on Engineering and Technology Research, 1, pp. 60–66. doi:10.62051/kvmd5645.