Structural discrepancy and domain adversarial fusion network for cross-domain fault diagnosis

Abstract

The current deep learning-based fault diagnosis methods have achieved excellent classification performance under supervised training, but the existence of domain discrepancy under different working conditions will greatly affect data relationship mining and fault diagnosis accuracy. Graph convolutional network (GCN) with topological structured relationships is excellent in solving these issues. In this paper, structural discrepancy and domain adversarial fusion network (SDDAFN) is proposed to learn deep high-dimensional features and eliminate distribution discrepancy across different domains. In data structure construction, the high-dimensional mapping features are first extracted accurately from the input raw signals by a feature encoder, then the output of encoder is converted into an instance graph through graph construction layer (GCL) for the convenient modeling by multi-channel kernel GCN (MCK-GCN). In network optimization, the classification loss function is introduced to optimize the discrimination boundaries of different fault types, and the integrated loss function based on structural discrepancy and domain adversarial are jointly adopted to minimize the domain distribution discrepancy. Experimental cases show the proposed method outperforms the state-of-the-art methods.