A Physics-Informed Single-Source Domain Generalization Framework for Bearing Fault Diagnosis Under Unseen Operating Conditions

Abstract

Unexpected breakdowns in rotating machinery can result in substantial economic losses and safety hazards, underscoring the critical need for reliable bearing fault diagnosis under variable operating conditions. Although deep learning has demonstrated a strong diagnostic capability, its practical deployment is often hindered by domain shifts caused by variations in speed or load, along with the difficulty of obtaining labeled data for all possible operating conditions. To address this challenge, a physics-informed single-source domain generalization (PI-SSDG) framework is proposed that learns from vibration signals collected from a single-source domain corresponding to a specific operating condition and effectively generalizes to unseen conditions without requiring target-domain data. The proposed method introduces a log-mean-removed, low-pass liftered cepstrum that explicitly suppresses global amplitude scaling induced by operating conditions and excitation-related periodicities while preserving fault-discriminative transfer-function-related features. In addition, a dual-branch architecture is designed that jointly exploits the preprocessed cepstrum and raw vibration signals, together with a joint training strategy using branch-specific losses that promotes complementary feature representations and mitigates residual domain variability. Extensive experiments conducted on three public bearing datasets demonstrate that the proposed method consistently achieves superior cross-domain accuracy under speed and load variations and outperforms recent domain-generalization baselines. Comprehensive ablation studies and feature visualization further confirm the effectiveness of the proposed components in improving diagnostic robustness.