A Bearing Fault Diagnosis Method Based on Improved Deep Residual Contraction Network

In this paper, a method for bearing fault diagnosis based on an improved deep residual contraction network is proposed. The method utilizes the residual contraction module in the deep residual contraction network, which is improved in combination with the Inception network, in order to enhance the diagnostic accuracy and efficiency of bearing faults. The method divides the bearing fault diagnosis problem into several sub-problems and designs the corresponding residual contraction module and Inception network structure for each sub-problem. Through experimental validation using an actual bearing fault dataset, the results demonstrate that the method achieves high accuracy and stability in bearing fault diagnosis, providing a new idea and method for research in the field of bearing fault diagnosis.