Fault Diagnosis of Electric Motors by a Channel-Wise Regulated CNN and Differential of STFT

Abstract

In various applications, the reliable and efficient detection of faults in electric machines is crucial, particularly in environments with high noise levels. To this end, the current study introduces an effective fault detection model utilizing the differential of Short-Time Fourier Transform (STFT) and a channel-wise regulated Convolutional Neural Network (CNN). The novel use of the differential of STFT is presented to enhance the diagnostic model's performance in noisy conditions compared with the conventional STFT. According to the inherent time-frequency domain information within the differential of STFT, a regulated CNN-based model is proposed to integrate spatio-temporal information into the feature map, thereby enhancing accuracy and reducing the computational demand. The method is evaluated on three datasets: the widely used Case Western Reserve University (CWRU) benchmark featuring bearing fault and vibration measurements, a dataset involving Permanent Magnet Synchronous Motor (PMSM) data with varying levels of Inter-Turn Short-Circuit (ITSC) fault and current measurements, and a dataset consisting of a mixture of mechanical and electrical faults. Comparative analysis highlights the superior performance of the proposed model over existing robust methods in the literature under both normal and noisy conditions.