关键词: 故障诊断, 滚动轴承, 仿真数据, 领域自适应

Abstract: To solve the problem that it was difficult to obtain a large number of high-quality rolling bearing fault data in the actual industrial environment， and the generalization performance of the intelligent diagnosis model was poor， a fault diagnosis method was proposed based on simulation data driven and domain adaptation. Firstly， a physical model was established to obtain rich simulation data， which simulated different failure forms of bearings according to bearing parameters and corresponding operating conditions. Secondly， the transfer learning method was used to solve the problem of inconsistent data feature distributions between simulation and actual fault data. The residual channel attention mechanism network was used to extract the transfer fault features of different domains， and the adaptive operation of different domains in the network training processes was carried out through the condition maximum mean discrepancy metric criterion， which taken into account the conditional distribution discrepancies between different domains. Finally， different transfer model tests were carried out on the bearing data sets damaged by man-made damage and accelerated life test. The results show that the method proposed may obtain better recognition accuracy when the target domain contains a small number of labels. 

Key words: fault diagnosis, rolling bearing, simulation data, domain adaptation