Szczegóły publikacji
Opis bibliograficzny
Bayesian Gaussian mixture model classifier for fault detection in induction motors using start-up current analysis / Kacper JARZYNA, Michał RAD, Paweł PIĄTEK, Jerzy BARANOWSKI // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073 . — 2026 — vol. 19 iss. 5 art. no. 1328, s. 1-26. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 24-26, Abstr. — Publikacja dostępna online od: 2026-03-06
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166765 |
|---|---|
| Data dodania do BaDAP | 2026-03-25 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en19051328 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
Induction motors constitute a major share of industrial drives, making reliable fault detection essential for maintaining operational continuity. This work develops a Bayesian classifier for identifying rotor-bar damage using start-up current measurements represented in the frequency domain. The spectra are modelled as smooth functional curves using a hierarchical B-spline formulation, and posterior sampling provides a generative mechanism for augmenting scarce labelled data. Classification is performed using a Bayesian Gaussian mixture model, where each prediction is obtained by averaging over thousands of posterior samples, yielding stable and interpretable probability estimates. In experimental evaluation, the proposed approach achieves consistent separation between healthy and faulty motors across repeated training runs, correctly identifying all test cases in the binary classification setting and exhibiting more stable probability estimates than logistic and soft-max regression under limited labelled data. The model additionally signals atypical responses for unmodelled faults, indicating potential for anomaly detection. These findings highlight the suitability of Bayesian functional modelling as a reliable tool for induction motor condition monitoring.
