Saliency-aware heterogeneous independence decoupling network for mechanical transfer fault diagnosis under unseen...

To mitigate the degradation in transfer diagnostic performance caused by unseen target domains in complex industrial scenarios, a novel saliency-aware heterogeneous independence decoupling network (SA-HIDN) is proposed for mechanical transfer fault diagnosis. Initially, a heterogeneous decoupled feature extractor is constructed to align the network capacity with the distinct physical attributes of signal components. It employs a deep branch with deep resonance shrinkage to perform soft-thresholding denoising for domain-invariant fault impulses, and a shallow branch with a statistical feature layer to explicitly capture global statistical moments as domain-specific condition features. Subsequently, a statistical independence constraint based on the Hilbert-Schmidt Independence Criterion (HSIC) is introduced to mathematically force the fault and condition subspaces to be independent in the reproducing kernel Hilbert space, effectively suppressing high-order information leakage between decoupled branches. Finally, a saliency-aware adaptive fusion (SAAF) module is developed to proactively protect subtle fault signatures and achieve high-fidelity feature reconstruction through a saliency-masking compensation mechanism. Extensive experiments on planetary transmission and train transmission system datasets demonstrate that the proposed SA-HIDN achieves transfer diagnostic accuracies of 98.13% and 94.48% on two datasets, respectively. These quantitative results and mechanistic analyses confirm that the proposed method significantly outperforms state-of-the-art methods while maintaining a favorable balance between diagnostic precision and computational cost.