Neural Networks Detect Inter-Turn Short Circuit Faults Using Inverter Switching Statistics

Early detection of an inter-turn short circuit fault (ISCF) can reduce repair costs and downtime of an electrical machine. In an induction machine (IM) driven by an inverter with a model predictive control (MPC) algorithm, the controller outputs are influenced by a fault due to the fault-controller interaction. Based on this observation, this study developed a neural network model using inverter switching statistics to detect the ISCF of an IM. The method was non-invasive, and it did not require any additional sensors. In the fault detection task, the model achieved an area under receiver operating characteristics curve value of 0.9992 (95% Confidence Interval: 0.9991 - 0.9992). At the rated operating conditions, it detected and located an ISCF of 2-turns (out of 104 turns per phase) under 0.1 seconds, a speedup of more than ten times compared to the thresholding-based method. Moreover, we published the switching vector data collected at various load torque and shaft speed values for healthy and faulty states of the IM, becoming the first publicly available ISCF detection dataset.