Bayesian and Classical Feature Ranking for Interpretable BLDC Fault Diagnosis
It provides incremental benchmark-specific evidence for interpretable fault diagnosis in BLDC motors, relevant to engineers and researchers in predictive maintenance.
This paper compared Bayesian and classical feature ranking methods for fault diagnosis in brushless DC motors, finding that Bayesian methods like ARD logistic and spike-and-slab achieved competitive balanced accuracies (e.g., 0.919-0.920 in binary classification) with smaller feature subsets, while classical methods like ReliefF reached up to 0.923.
This paper compares Bayesian and classical feature ranking methods for interpretable fault diagnosis of brushless DC (BLDC) motors. Two Bayesian approaches, spike-and-slab and ARD logistic ranking, are evaluated against three classical baselines on a public BLDC benchmark in binary and multiclass settings using current-based, rotational-speed-based, and combined feature sets. The strongest overall results are obtained for the combined representation. In binary classification, ReliefF achieves the highest balanced accuracy of 0.923, while ARD logistic and spike-and-slab remain very close at 0.919 and 0.920 with much smaller subsets ($k=5$). In multiclass classification, ARD logistic performs best for the combined variant with balanced accuracy 0.914, followed closely by LASSO (0.913) and spike-and-slab (0.912). The results show that Bayesian ranking is particularly competitive for current-only and combined descriptors, while ReliefF remains especially effective for speed-based ranking. Because the benchmark consists of short segmented observations from a limited number of experimental conditions, the findings are interpreted primarily as benchmark-specific evidence rather than strong claims of fault generalization.