Szczegóły publikacji
Opis bibliograficzny
Detecting faults in electric motors based on current and rotor speed measurement using a naive Bayesian classifier / Waldemar BAUER, Kacper Jarzyna, Paweł PIĄTEK, Jerzy BARANOWSKI // W: MMAR 2025 [Dokument elektroniczny] : 29th international conference on Methods and Models in Automation and Robotics : 26–29 August 2025, Międzyzdroje, Poland : technical papers : on line proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2025. — ( International Conference on Methods and Models in Automation and Robotics ; ISSN 2835-2815 ). — USB ISBN: 979-8-3315-2648-1. — Print on Demand(PoD) ISBN: 979-8-3315-2650-4. — e-ISBN: 979-8-3315-2649-8. — S. 261–265. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 265, Abstr.
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 162287 |
|---|---|
| Data dodania do BaDAP | 2025-09-11 |
| Tekst źródłowy | URL |
| DOI | 10.1109/MMAR65820.2025.11150867 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | International Conference on Methods and Models in Automation and Robotics 2025 |
| Czasopismo/seria | International Conference on Methods and Models in Automation and Robotics |
Abstract
Automatic fault detection in electric motors is essential for maintaining reliability and reducing downtime in industrial applications. This paper presents a novel approach that applies a Gaussian Naive Bayes classifier to high-frequency measurements of current and rotor speed to identify motor faults. Data were collected from a dedicated test bench at AGH University of Krakow, where motors were evaluated under healthy conditions as well as under mechanical, electrical, and combined damage scenarios. Statistical features were extracted from the time-series signals and used to train the classifier. Experimental results indicate that the classifier achieved detection accuracies of approximately 76 percent using current data and 85 percent using rotor speed data, with similar performance when both modalities were combined. These findings demonstrate the potential of probabilistic models for real-time fault diagnostics and lay the groundwork for future research into advanced signal fusion and classification techniques.
