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 BLDC/PMSM drives. Discovery analysis on the public DUDU-BLDC benchmark compares current, speed, and combined representations under explicit top-k budgets using ReliefF, mRMR, LASSO, and Bayesian automatic relevance determination logistic ranking. A second March 2026 experimental campaign, released as DUDU-BLDC 1.5, comprises 50 recordings from five physical motors under altered acquisition conditions. On this second campaign, five-fold recording-grouped validation with three deterministic repeats reached balanced accuracies of 0.806 and 0.779 for the two confirmatory within-motor tasks. Motor-held-out balanced accuracies fell to 0.512 and 0.604, with most physical-motor estimates near chance, exposing substantial between-motor heterogeneity. A paired experiment that quantised the DUDU-BLDC 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.