A dual attention mechanism network with self-attention and frequency channel attention for intelligent diagnosis of multiple rolling bearing fault types

Abstract

Different fault types of rolling bearings correspond to different features, and classical deep learning models using a single attention mechanism (AM) have limitations in capturing feature diversity. Therefore, a novel dual attention mechanism network (DAMN) with self-attention (SA) and frequency channel attention (FCA) is proposed for rolling bearing fault diagnosis. The SA mechanism is used to capture global relationships between the input features and fault types, and the FCA mechanism applies multi-spectral attention to learn the local useful information among different input channels. The results of the ablation study on the effects of FCA blocks showed that including a proper combination of multiple frequency components is helpful in achieving higher accuracy. Experiments were conducted to diagnose rolling bearings with multiple types of faults. The results show that, compared with current fault diagnosis models, the proposed DAMN has better comprehensive performance in terms of diagnosis accuracy and model convergence speed. It was also demonstrated that the backbone of DAMN based on a dual AM could achieve better performance than the backbone based on a single AM.