Research on early fault detection of current sensors operating in a closed-loop control structure using transfer learning

This research is focussed on transfer learning used to develop universal algorithms to detect various faults of current sensors in the AC drive system. It presents the possibility of using the idea of deep convolutional network transfer learning in fault diagnosis of current sensors of induction motors operating in a closed control structure. The versatility of the approach includes the possibility, as demonstrated in the article, of expanding the range of current sensor faults to be analyzed. In the study, a drive system simulation model was used as a source of diagnostic information. Particularly notable is the very high level of precision of the developed system (more than 98.3%) at the same time as the possibility of extending the types and levels of considered faults. Furthermore, the response time to an emerging fault was less than 0.0125 seconds.