Bayesian and Classical Feature Ranking for Interpretable BLDC Fault Diagnosis

TL;DR

This study compares Bayesian and classical feature ranking methods for BLDC motor fault diagnosis, finding Bayesian approaches competitive especially with combined feature sets, but results are benchmark-specific.

Contribution

It evaluates Bayesian and classical feature ranking methods on a BLDC fault diagnosis benchmark, highlighting their relative performance and applicability.

Findings

ReliefF achieves highest binary classification accuracy (0.923).

ARD logistic performs best in multiclass classification with combined features (0.914).

Bayesian ranking is competitive for current and combined descriptors.

Abstract

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.