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