Stator Winding Faults Diagnosis in Induction Motor Based on ANN and ANFIS

This paper proposes a new method using Artificial Neural Network (ANN) and Adaptive Neural Fuzzy Inference System (ANFIS) for diagnosis Inter Turn Short Circuit (ITSC) faults in induction motor. The proposed diagnosis procedure is based on the analysis of stator current. The study includes a comparative analysis of a various diagnostic methods such as decision tree, k-nearest neighbours, naive bayes, random forest and support vector machine. The time domain features extraction pre-processes the input data before entering to classifier model. The test accuracy and cross-validation analysis evaluate the model efficiency. The most important time domain features are selected to improve the performance of the classifier. ANN based the most important time domain features gives better performance as compared to all others classifier. This paper presents a comparison between ANN and ANFIS based on the most important time domain features, auto-regressive model and discrete wavelet transform. ANFIS based discrete wavelet transform achieves higher performance than ANN. The laboratory experiments on 1.5 HP squirrel cage induction motor under different loading conditions verify the proposed technique efficiency to diagnose fully various ITSC faults