An optimized classifier chains‐based deep learning framework for Inter-Turn Fault diagnosis in Permanent Magnet Synchronous Motors

Abstract

Inter-Turn Faults (ITF) of Permanent Magnet Synchronous Motor (PMSM) pose a major challenge. Early detection of these faults improves PMSM performance for predictive maintenance, preventing performance drops and reducing maintenance costs. This paper introduces a new model for the automatic detection of ITF, utilizing an optimized convolutional neural network (CNN). The proposed model incorporates convolutional layers for feature extraction, normalization layers to achieve better convergence, dropout layers to avoid overfitting, and bi-long short-term memory layers (LSTM) to preserve temporal dependencies. The LSTM layers of CNN aid in time series data analysis. Furthermore, Bayesian optimization is used to automatically select and optimize the CNN model’s parameters and improve its performance. This system has several outputs to identify the fault types and their exact location. The classifier chain technique is utilized to maintain independence between different outputs, thereby increasing the system’s accuracy and efficiency. The data used in this study includes the phase currents of the PMSM in healthy and faulty conditions with different intensities. Our proposed model is designed as a multi-output system and can detect both the fault type, such as the fault from phase A to ABC, and the fault locations in three phases, ranging from 10 % to 90 %. Additionally, this model’s performance, along with other models considered for comparison, has been evaluated using various criteria such as accuracy and F1-score to testify to the effectiveness of the proposed method. The results indicate that the proposed optimized CNN model can automatically detect stator ITFs with an accuracy higher than 95 %.