PhysiCausalNet: A Causal- and Physics-Driven Domain Generalization Network for Cross-Machine Fault Diagnosis of Unseen Domain

Abstract

Domain generalization for intelligent fault diagnosis is a technology that can acquire diagnostic knowledge from related machines and generalize to the unseen domain. However, the structure and working conditions differences between machines lead to significant variation in data distribution, making it difficult to generalize the trained network directly to unseen machines. This research proposed PhysiCausalNet, a causal- and physics-driven domain generalization network that mines the fault causality and incorporates the physical prior knowledge of the unseen target machine to realize domain-invariant feature extraction and domain-specific knowledge embedding. To form the cross-domain invariant causal mechanism, the progressive consistency causal factorization loss is proposed to separate the fault causal factors from implicit representation. Meanwhile, for the adaptability to a specific domain without involving the target domain data, the Fourier filter demodulation structure is proposed to extract periodic fault components, and the dynamics embedding loss is designed according to prior physical knowledge of the target machine as a physical constraint for the network. The effectiveness of proposed approach is verified in four machines and twelve working conditions including public, laboratory, and industrial datasets.

Topics & Concepts