Szczegóły publikacji
Opis bibliograficzny
Bayesian and classical feature ranking for interpretable BLDC fault diagnosis / Waldemar BAUER, Jerzy BARANOWSKI // W: IEEE GPECOM 2026 [Dokument elektroniczny] : 8th Global Power, Energy and Communication Conference : Naples, Italy, 3-5 June, 2026 : proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2026. — ( Global Power, Energy and Communication Conference ; ISSN 2832-7675 ). — Print on Demand(PoD) ISBN: 979-8-3315-5205-3. — e-ISBN: 979-8-3315-5204-6. — S. 262–267. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 267, Abstr. — Publikacja dostępna online od: 2026-07-02
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 169110 |
|---|---|
| Data dodania do BaDAP | 2026-09-10 |
| Tekst źródłowy | URL |
| DOI | 10.1109/GPECOM70462.2026.11578644 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Czasopismo/seria | Global Power, Energy and Communication Conference |
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.
