Szczegóły publikacji
Opis bibliograficzny
Detection of electric motor damage through analysis of sound signals using Bayesian neural networks / Waldemar BAUER, Marta Zagórowska, Jerzy BARANOWSKI // W: IECON 2024 [Dokument elektroniczny] : 50th annual conference of the IEEE Industrial Electronics Society : Chicago, USA, 3–6 November 2024 : proceedings. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2024. — Print on Demand (PoD) ISBN: 978-1-6654-6455-0. — e-ISBN: 978-1-6654-6454-3. — S. 1–5. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 4–5, Abstr. — Publikacja dostępna online od: 2025-03-10
Autorzy (3)
- AGHBauer Waldemar
- Zagórowska Marta
- AGHBaranowski Jerzy
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 159004 |
|---|---|
| Data dodania do BaDAP | 2025-04-30 |
| Tekst źródłowy | URL |
| DOI | 10.1109/IECON55916.2024.10905763 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided.
