Electrical fault detection in three-phase induction motor using deep network-based features of thermograms

Abstract

In this paper, an automatic method is proposed for detecting the operating faults in three-phase induction motors based on thermal images. If these faults are not detected or fixed on time, they can lead to permanent motor failure. This is why non-invasive and non-destructive experiments are significantly considered. In this paper, first, the region of interest is detected in the thermograms using SIFT-based key-points matching. Then, these images are transformed into representative feature vectors based on a pre-trained convolutional neural network. Then, the training vector samples are clustered into cold and hot clusters by K-means. For each cluster, an SVM-based classifier is trained. The test feature vector samples are clustered and mapped into classes using the corresponding trained SVM-based classifiers. Evaluating the proposed method on the datasets including real thermal images, shows that this algorithm can detect 100% of the faults of the induction motor.