Abstract:  Aiming at the problems that complex signal processing, expert knowledge and artificial construction algorithms and other technical means were needed in traditional bearing fault diagnosis methods and few fault data of mechanical equipment was available in engineering practice, a rolling bearing fault diagnosis method based on AlexNet and Transfer Learning was put forward by taking the original vibration signal of rolling bearing in normal operation and different faults as identification basis. The collected original vibration data of rolling bearings were converted into vibration signal diagrams, and then the labels for the vibration signal diagrams were set as training samples. The pretrained AlexNet network was finetuned to meet the task requirements, and the prepared training samples were used to train the adjusted network. The data set of the Bearing Data Center of Case Western Reserve University in the United States was used to verify the performance of the network model, and the diagnosis accuracy of 100% under three categories, inner ring fault, outer ring fault and rolling element fault, was obtained. The research results show that this method can realize the diagnosis of common fault types of rolling bearings even when the marked fault data is scarce, and the diagnosis accuracy improves comparing with existing advanced methods.

Key words: rolling bearing;fault diagnosis;AlexNet;transfer learning

YUAN Lao-hu, CHEN Yuan-qiang, DU Bai-yu, et al. Fault diagnosis of rolling bearing based on AlexNet and transfer learning［J］.Journal of Mechanical & Electrical Engineering, 2021,38(8):1016-1022.