A new multichannel deep adaptive adversarial network for cross-domain fault diagnosis

Abstract Currently, most fault diagnosis methods can achieve desired results from a single signal source. However, a single sensor signal has limited features and adaptability to the working environment, which will greatly affect the diagnosis results. To overcome this weakness, a multichannel deep adaptive adversarial network (MCDAAN) based on fusing acoustic and vibration signals is proposed in this paper. The training process of MCDAAN primarily includes the following aspects. First, the acoustic and vibration signals extracted by the neural network feature extraction are fused after being adjusted by the convolutional block attention module in channel and spatial dimensions. Next, the fusion features of the source and target domains are measured by the Wasserstein distance. Finally, the fused features are classified by the label and domain classifiers. The proposed MCDAAN is tested using acoustic and vibration signals collected at ten transfer tasks. The results demonstrate that the diagnostic accuracy of the proposed MCDAAN can reach more than 99% in both groups of experiments. MCDAAN can accurately classify all kinds of fault samples, and the classification accuracy is superior to other comparison methods.