Neural Networks Detect Inter-Turn Short Circuit Faults Using Inverter
Switching Statistics for a Closed-Loop Controlled Motor Drive
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 neural network models 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, an area
under receiver operating characteristics curve value of 0.9950 (95%
Confidence IntervaI: 0.9949 - 0.9951) was obtained. At the rated
operating conditions, the neural network model detected and located an
ISCF of 2-turns (out of 104 turns per phase) under 0.1 seconds, a
speedup of more than two 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.
Together with the dataset, we provided performance baselines for three
main neural network architectures, namely, multi-layer perceptron,
convolutional neural network, and recurrent neural network.