Neural Networks Detect Inter-Turn Short Circuit Faults Using Inverter Switching Statistics for a Closed-Loop Controlled Motor Drive

• Published in: IEEE transactions on energy conversion Vol. 38; no. 4; pp. 2387 - 2395
• Main Authors: Oner, Mustafa Umit, Sahin, Ilker, Keysan, Ozan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Closed loops; Condition monitoring; Confidence intervals; Controllers; Datasets; Fault detection; Fault diagnosis; induction motor; Induction motors; Inverters; Machine learning; model predictive control; motor drives; multi-layer perceptron; Multilayer perceptrons; Multilayers; Neural networks; Predictive control; Recurrent neural networks; Short circuits; Switching
• DOI: 10.1109/TEC.2023.3274052
• ISSN: 0885-8969

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 Interval: 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.