ANN-based fault diagnosis of induction motor under stator inter-turn short-circuits and unbalanced supply voltage

Induction motors (IMs) are extensively used in industrial sector. This kind of machine is subjected to several stresses that could interrupt their normal operation. An excessive stress can generate some symptoms before the IM fall in failure situation. Therefore, incipient detection of these symptoms permits the shutdown of IMs in order to avoid total destruction. Fault detection is then the main objective of diagnosis systems. Stator inter-turn short-circuits (SITSC) constitutes an important amount of cause of IM breakdown. However; unbalance supply voltage (USV) is one of the advantageous factors that affect IMs operation. Thus, in order to avoid false alarm induced by USV, the diagnosis system must make difference between USV and SITSC faults. This paper presents an efficient approach to estimate SITSC percentage and detect USV occurrence using Artificial Intelligent (AI) tool. Artificial neuronal network (ANN) plays the key-role of the proposed diagnosis system. A fault Classifier of SITSC and USV is carried out using multi-layer perceptron neuronal network (MLP-NN). The training, testing and validation phases of MLP-NN need the dataset creation. The required data is obtained from both simulated mathematical model of IM and laboratory test-bed. The reached results show the sensitivity and the well-functioning of the proposed diagnosis system.