Trust-Aware Domain Adaptation Using Physics-Guided Reliability Learning for Cross-Condition Fault Diagnosis of Milling Machines.

Reliable fault diagnosis of milling machines under varying operating conditions remains challenging due to distribution shifts caused by speed variations, nonstationary dynamics, and limited labeled data in target domains. Conventional domain adaptation methods often assume equal reliability across samples and neglect the varying physical consistency of signals collected under different conditions. To address this limitation, this study proposes trust-aware domain adaptation network for cross-domain fault diagnosis that integrates physics-guided reliability estimation with deep representation learning. In the proposed framework, physically interpretable global and local features are first extracted from multi-channel vibration signals using energy, spectral, nonlinear, and impulsiveness descriptors. A dedicated Physics Trust Network is then introduced to estimate per-sample trust scores that quantify the physical reliability of each signal based on its physics feature consistency. These trust scores are explicitly embedded into representation learning through a trust-weighted feature encoder, ensuring that physically reliable samples contribute more strongly to the learned latent space. To address distribution mismatch between source and target conditions, a trust-weighted covariance alignment strategy is introduced, enabling domain adaptation to be guided by reliable samples instead of treating all data equally. In this way, the model simultaneously learns discriminative, transferable, and physically consistent features. The entire framework is trained end-to-end using labeled source data and unlabeled target data, enabling effective knowledge transfer under cross-speed conditions. Extensive experiments on a real milling machine dataset collected at different spindle speeds demonstrate that the proposed framework achieves an average accuracy of 98.07%, performing better than two recent state-of-the-art domain adaptation approaches by a significant margin. Ablation experiments further confirm that reliability estimation, trust-weighted representation learning, and trust-guided alignment each contribute independently to performance improvement.