A Physics-Guided Dual-Sensor Framework for Bearing Fault Diagnosis in PMDC Motor Drives

Rolling-element bearing faults are a primary mechanical failure mode in rotating systems. In Permanent Magnetic DC (PMDC) motor applications operating under variable torque, vibration-based diagnosis is affected by load-dependent excitation and commutation-induced disturbances, which introduce amplitude bias and reduce the reliability of conventional statistical features. This study proposes Cross-Reference Energy Attention (CREA), a physics-guided dual-sensor feature framework for three-class bearing states in PMDC motor systems. CREA isolates fault-relevant content within a hardware-agnostic, empirically selected mid-frequency carrier band and incorporates a spatially separated reference sensor to evaluate transmission consistency. This design suppresses disturbances generated locally by the motor while retaining structurally transmitted bearing signatures. Experiments were conducted on a PMDC motor dynamometer with seeded bearing defects under controlled torque variation. GroupKFold cross-validation was implemented using the acquisition run as the grouping variable to prevent data leakage across runs. Under per-run normalization designed to eliminate amplitude memorization, conventional motor-side baseline features degraded to 0.495 ± 0.110 window-level accuracy, whereas the four-feature CREA representation maintained 0.999 ± 0.002. Systematic ablation and SHAP analysis demonstrate that carrier-band energy features provide the dominant discriminatory contribution, while cross-sensor interaction metrics supply complementary transmission validation consistent with the underlying mechanical model.