On Bayesian Optimization-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM

Abstract

Interturn short circuit (ITSC) fault diagnosis at its early stage is very essential to improve the security of permanent magnet synchronous motors. In this article, a Bayesian optimization (BO) based residual convolutional neural network (CNN) algorithm for ITSC fault diagnosis was proposed. There are mainly two aspects of the contributions. First, it is a challenge to apply a conventional CNN on time-series signal tasks as they can only look back on history with a liner size, which will limit the network depth. Moreover, to obtain enough characteristics from acquired signals, the proposed algorithm requires adequate network depth, which may result in degradation difficulties. To solve these problems, a residual connection is embedded into the dilated CNN. Second, with the increase of network depth, the possible combination of hyperparameters will grow geometrically, thus the hyperparameters tuning will cost considerable time. To overcome this, the BO algorithm is adopted to select the optimal hyperparameter combination automatically. To demonstrate the effectiveness of the proposed algorithm, a motor fault experiment with various operating conditions was conducted on the motor that can be set for 17 fault severity levels. The test results and comparisons with other five algorithms show the advantage of the proposed algorithm.