Data-driven fault monitoring for spacecraft control moment gyro with slice residual attention network

This paper studies the fault monitoring problem of a spacecraft control moment gyro (CMG) in complex environments based on the data-driven method. First, the wavelet denoising method and short-time Fourier transform (STFT) are utilized to preprocess the signal measured by an industrial personal computer (IPC) to obtain the frequency spectrum of each failure mode. Then, a slice residual attention network (SRAN) based on the ResNeXt model, attention mechanism, and random slice idea is proposed, which can fully capture the edge features of images while satisfying the learning efficiency. Furthermore, a set of comparative experiments are carried out to validate the ability of the proposed method, and the performance of SRAN is further verified under different datasets. Finally, based on the confusion matrix and t-SNE dimension reduction technique, the monitoring ability of SRAN for various faults is analyzed. Experimental results show that SRAN processes good fault monitoring capability and ideal robustness and can identify different fault degrees under the real-time fault monitoring scenario.