A novel approach of fault diagnosis based on multi-source signals and attention mechanism

The health condition of industrial equipment is closely related to productivity and safety, attaching great importance to fault diagnosis. Although current fault diagnosis methods have already achieved good effect to some degree, it is more reliable and outstanding to utilize multiple signal sources and pay attention to the effective information differences between various signals. In order to take full advantage of fault information, this paper proposes a new method of fault diagnosis based on multi-source signals and attention mechanism. This method learns the weights of multi-source features with attention mechanism and recalibrates the feature responses. Besides it extracts and fuses the fault features based on residual network (ResNet). The experimental results show that the proposed method can effectively learn the differences of multisource signals, and has excellent classification performance compared to those based on a single signal or multiple signals without attention mechanism.