Lifting wavelet-informed hierarchical domain adaptation network: An interpretable digital twin-driven gearbox fault diagnosis method

Abstract

Digital twin (DT) has served as a dependable technology for supplementing reliable simulated fault data in gearbox fault diagnosis. However, the vast data distribution discrepancy and insufficient interpretability still significantly limit the industrial application of DT-driven fault diagnosis methods. To solve these problems, a lifting wavelet-informed hierarchical domain adaptation network (LHDAN) is proposed for transferring the diagnostic knowledge between the physical gearbox and DT model. LHDAN improves the interpretability of diagnostic knowledge transfer in terms of parameter initialization, physical constraints on the training process, and feature distribution adaptation. Specifically, LHDAN utilizes a lifting wavelet-informed convolutional neural network (LW-Conv) to mimic the cascade structure of lifting wavelet decomposition, in which the fully learnable prediction and update operators are initialized with existing wavelet bases and further constrained with high-pass and low-pass filters in the training process. Furthermore, a kurtosis-guided attention mechanism is proposed to fuse hierarchical features with diverse transferabilities flexibly. Finally, the fused hierarchical features of the actual gearbox and DT model are explicitly aligned to eliminate the feature distribution discrepancies. A high-fidelity DT model is established based on an industrial gearbox fault test bench. Compared to several state-of-the-art models, LHDAN demonstrates superior interpretability and diagnostic performance.