Severity Estimation of Inter-Turn Short-Circuit Fault in PMSM for Agricultural Machinery Using Bayesian Optimization and Enhanced Convolutional Neural Network Architecture

The permanent magnet synchronous motor (PMSM) is a key power component in agricultural machinery. The harsh and variable working environments encountered during the operation of agricultural machinery pose significant challenges to the safe operation of PMSMs. Early diagnosis of inter-turn short-circuit (ITSC) faults is crucial for improving the safety of the motor. In this study, a fault diagnosis method based on an improved convolutional neural network (CNN) architecture is proposed, featuring two main contributions. First, a dilated convolutional neural network is combined with residual structures, multi-scale structures, and channel attention mechanisms to enhance the training efficiency of the model and the quality of feature extraction. Second, Bayesian optimization algorithms are applied for the automatic tuning of architecture hyperparameters in deep learning models, achieving automatic optimization of the hyperparameters for the fault diagnosis model of ITSCs. To validate the effectiveness of the proposed algorithm, 17 simulated tests of ITSC fault severities were conducted under both constant conditions and dynamic conditions. The results show that the proposed model achieves the best performance regarding the validation accuracy (98.2%), standard deviation, F1 scores, and feature learning capability compared to four other models with different architectures, demonstrating the effectiveness and superiority of the algorithm.