Physics guided semantic consistency learning for bearing fault diagnosis in agricultural machinery under operating...

Rolling bearings in agricultural machinery operate under pronounced operating-condition shifts, such as speed and load fluctuations and contamination-related noise, which often induce a distribution mismatch between training and deployment signals. This work studies bearing fault diagnosis under a source-only, single-source domain generalization (DG) setting, where the model is trained and selected using only source-domain data, and samples from the target operating condition or target dataset are not used for training, validation, hyperparameter selection, band-pass selection, or early stopping. We formulate cross-condition robustness as a semantic consistency problem between two complementary representations: an analytic mechanism-oriented representation emphasizing impact-related resonance demodulation, and a data-driven temporal representation learned from raw waveforms. A dual-path framework is developed accordingly. The analytic path learns a differentiable soft band-pass mask to localize an informative resonance band and constructs an impulse-oriented descriptor from statistics of the band-pass signal, its Hilbert envelope, and squared-envelope energy. The temporal path encodes normalized raw segments using a lightweight dilated one-dimensional convolutional network with temporal-attention pooling. The two embeddings are fused by a sample-wise gate, with an entropy penalty used to discourage near-uniform averaging. This design aims to improve cross-condition generalization by using the analytic path as a mechanism-oriented semantic anchor, constraining the temporal path through cross-view agreement, and allowing the fused representation to adapt to sample-dependent reliability changes. To reduce representation drift under regime changes, the two views are aligned using a bidirectional InfoNCE objective with a learnable temperature that adapts similarity scaling across operating conditions. A mechanism-critical control is also reported: replacing the analytic path with same-dimensional non-mechanistic features consistently degrades cross-condition performance, indicating that the analytic anchor is not interchangeable with generic auxiliary branches. Experiments on CWRU, SEU, and an agricultural-machinery-relevant test-rig dataset show in-domain accuracies of 99.48%, 98.50%, and 97.53%, respectively. In strict cross-speed evaluation on the test-rig dataset, the method achieves 98.22% accuracy when trained at 1500 r/min and tested at 2000 r/min, and 98.03% accuracy in the reverse setting. In cross-dataset evaluation using the shared normal, inner-race, outer-race, and rolling-element fault classes, the proposed method achieves the best average performance among the evaluated source-only baselines, including generic DG methods and recent bearing-diagnosis generalization methods. These comparisons include DPICEN and a protocol-matched single-source adaptation of FARNet, denoted FARNet-SS, both evaluated without target-domain access during training or model selection. The model remains lightweight, with 0.1348 M parameters and 127.11 M FLOPs for the neural forward pass.