TOPIC：A deep transfer learning fault diagnosis method for mine rotating machinery
ABSTRCT：An effective fault diagnosis method is of great significance to improve the safety of mine production. Despite the considerable success of the deep learning the absence of components failure data limits the performance of the model. In this paper, a fault diagnosis method is proposed to solve the problem of lacking in fault data. In the method, a transfer learning strategy is presented to confusion actual and labratory data distrbution from the perspective of model to solve fault data lacking. Fault diagnosis experiments conducted on test bed are carried out. And actual working condition data are collected from scraper converyor. The proposed method obtains % fault diagnosis accuracy in actual bearing data sets, which the generalization performance outperforms tranditional methods.