Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG

Predictive maintenance in the permanent magnet synchronous motor (PMSM) is of paramount importance due to its usage in electric vehicles and other applications. Recently various deep learning techniques are applied for fault detection and identification (FDI). However, it is very hard to optimally train the deeper networks like convolutional neural network (CNN) on a relatively fewer and non-uniform experimental data of electric machines. This paper presents a deep learning-based FDI for the irreversible-demagnetization fault (IDF) and bearing fault (BF) using a new transfer learning-based pre-trained visual geometry group (VGG). A variant of ImageNet pre-trained VGG network with 16 layers is used for the classification. The vibration and the stator current signals are selected for the feature extraction using the VGG-16 network for reliable detection of faults. A confluence of vibration and current signals-based signal-to-image conversion approach is also introduced for exploiting the benefits of transfer learning. We evaluate the proposed approach on ImageNet pre-trained VGG-16 parameters and training from scratch to show that transfer learning improves the model accuracy. Our proposed method achieves a state-of-the-art accuracy of 96.65% for the classification of faults. Furthermore, we also observed that the combination of vibration and current signals significantly improves the efficiency of FDI techniques.