Research on intelligent diagnosis of mechanical rolling bearing faults through transfer learning

Introduction This article proposes a fault diagnosis algorithm for mechanical rolling bearings based on transfer learning. Methods The proposed algorithm enhances the traditional conventional convolutional neural network (CNN) algorithm by introducing a domain category judgment module and an inter-domain conditional probability distribution difference module, thereby achieving transfer learning between source domain samples and target domain samples. Simulation experiments were performed. On a PT100 bearing fault simulation test platform, vibration signals of bearings were collected in cases of normal operation, inner race faults, outer race faults, and ball faults at motor speeds of 1,000, 1,500, and 2,000 r/min. The diagnostic performance of support vector machine (SVM), back-propagation neural network (BPNN), and the proposed algorithm was evaluated in operating condition transfer tasks. Moreover, ablation experiments were conducted. Results It was found that the proposed algorithm could effectively and accurately identify bearing faults in the face of changes in operating conditions. Discussion Both the domain category judgment module and the inter-domain conditional probability distribution difference could effectively achieve transfer learning of the diagnostic model.