Domain-conditioned feature modulation for cross-condition fault diagnosis in rotating machinery

Abstract Rotating machinery operating under varying speeds and loads exhibits strong nonstationarity, causing distribution shifts between training and deployment conditions that hinder reliable cross-condition fault diagnosis. Most domain generalization (DG) methods attempt to address this issue by enforcing domain-invariant representations; however, in rotating machinery, operating-condition variations are often physically coupled with fault-related features, making strict invariance assumptions less suitable—particularly when source domains are limited or discrepancies are large. To overcome these limitations, we propose a domain-conditioned dynamic DG framework for fault diagnosis under unseen operating conditions. The method introduces an explicit domain embedding branch and a lightweight hypernetwork with feature-wise linear modulation to generate channel-wise modulation parameters. By conditioning task features on operating information, the model adaptively adjusts its decision behavior across domains without adversarial training or explicit distribution alignment. Experiments on four public datasets (CWRU bearings, HUST bearings, HUST gearboxes, and PHM2009 gearboxes) demonstrate that the proposed framework achieves competitive or superior multi-source generalization performance and exhibits consistently improved training stability across diverse tasks and hyperparameter settings.