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

Abstract

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.

Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

“…Vibration measurements were carried out for bearings with modelled damage to the outer and inner raceways and rolling elements, and for bearing without a defect, using the DeltaTron 4506 accelerometer by Brüel and Kjaer. Additionally, bearings under testing were damaged by a spark jump to the selected element (electric discharge between the electrode and the damaged element) similar to [1,8,13,31]. In the literature, bearing failures are often presented with a cut raceway or a hole made in the damaged raceway [7,13,17,18,23,25].…”

“…This is due to their numerous advantages, such as high efficiency, possibility of high torque overload, and high power-to-weight ratio. They work well in applications that require both high precision and high dynamics [1,2]. Reliability is an equally important requirement for a modern drive system.…”

“…Therefore, methods are being sought to eliminate this human factor and automate the detection process. At present, this task is more often performed by neural networks (NNs), which can act as detectors and/or classifiers of damages, minimizing human participation in the diagnostic process [1,5,[14][15][16][17][18][19][20][21][22][23].…”

“…Recently, in the research on the detection of damage to rolling bearings in AC motor drives, frequently, deep neural networks (DNNs) are proposed, which are gaining more popularity due to the development of deep learning methods [1,15,16]. In Reference [1], the diagnostics of rolling bearings of the PMSM drive using a convolutional neural network (CNN) was presented, based on the measurement of stator current and vibration signals. Simultaneous analysis of stator current frequency spectrum and raw vibration signal allowed Energies 2021, 14, 712 3 of 24 obtaining detection efficiency at the level of 96%.…”

“…The effectiveness was tested for variable speed and load torque values. The problem was treated in a binary way, thus the network response showed only failure (1) or healthy (0) operating conditions. The best results, 100% efficiency, was obtained for the highest load and speed values.…”