Stable and Compact Diagnostic Signatures for Demagnetization-Related Faults in BLDC/PMSM Drives: Evidence from Two Measurement Campaigns

This paper investigates stable and compact diagnostic feature signatures for faults related to demagnetization in brushless direct-current (BLDC) and permanent-magnet synchronous motor (PMSM) drives. Discovery analysis on the public DUDU-BLDC v1 benchmark—where DUDU is the project name derived from the PolishDiagnostyka Uszkodzeń i Degradacji Urządzeń—compares current, speed, and combined representations under explicit top-k budgets using ReliefF, minimum-redundancy maximum-relevance (mRMR), least absolute shrinkage and selection operator (LASSO), and Bayesian automatic relevance determination (ARD) logistic ranking. The revision is accompanied by DUDU-BLDC 1.5, a new and previously unpublished March 2026 dataset comprising 50 recordings from five physical motors under altered acquisition conditions. On this second campaign, leakage-free nested five-fold recording-grouped validation with three deterministic repeats reached balanced accuracies of 0.760 and 0.758 for the two confirmatory within-motor tasks. Motor-held-out balanced accuracies fell to 0.500 and 0.554, with four of five physical-motor estimates at chance and one estimate at 0.663, exposing substantial between-motor heterogeneity. A paired experiment that quantized the original raw current signals to the approximately 0.08 A resolution of DUDU-BLDC 1.5 produced a mean absolute balanced-accuracy change of 0.0046, although the maximum change was 0.0725 and individual ranking-stability changes were larger. The results support compact signatures for repeated monitoring within an established or calibrated motor population and show aggregate robustness to reduced current resolution. They do not establish universal transfer to unseen motor instances; broader deployment requires motor-specific calibration or more diverse multi-motor training data.