An Unsupervised Domain Adaption Method for Fault Diagnosis via Multichannel Variational Hypergraph Autoencoder

Sensors ensure the normal operation of the system by monitoring and collecting large amounts of data in real-time. Due to the complexity and variability of the working environment, the industrial system exhibits different behaviors under different working conditions. Existing research methods mainly rely on graph structure to learn one-to-one connections between samples, and there is a lack of research on higher order features and contextual data. And changes in the current moment of time series data may have the same impact on the next moment and some time in the future. Therefore, we propose a multichannel variational hypergraph autoencoder (MC-VHAE) network for unsupervised domain adaptation (UDA) fault diagnosis across working conditions. In the proposed multichannel network, on one hand, convolutional neural networks are used to extract multiscale features from time-domain fault data. On the other hand, discrete wavelet transform (DWT) is used to separate the high-frequency and low-frequency components of the fault sample in the time-frequency domain. The potential higher order information is extracted by variational hypergraph autoencoder (VHAE). In VHAE, a multiorder neighborhood hypergraph convolutional layer (MON-HGCL) is designed to aggregate the high-order feature information of different order neighborhoods in the hypergraph nodes. Finally, the feature fusion layer is used to retain the low-frequency trend component and multiscale features while removing high-frequency noise components. Experimental results show that MC-VHAE outperforms existing methods and demonstrates its ability to extract domain-invariant features under different operating conditions.