Szczegóły publikacji
Opis bibliograficzny
Mixture based classifier using Gaussian processes for induction motor diagnosis / Adrian DUDEK, Kacper Jarzyna, 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. 1–6. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 5–6, Abstr. — Publikacja dostępna online od: 2025-03-10
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 159012 |
|---|---|
| Data dodania do BaDAP | 2025-04-30 |
| Tekst źródłowy | URL |
| DOI | 10.1109/IECON55916.2024.10906040 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
This paper explores the use of Gaussian Processes (GPs) and Gaussian Mixture Models (GMMs) for diagnosing induction motors. GPs provide flexible, non-linear models that handle noisy data, while GMMs offer robust probabilistic classification by modeling data as mixtures of Gaussian distributions. By integrating Bayesian inference with GMMs and utilizing Stan for complex model management, the study enhances classification accuracy. Experimental data from induction motors under various conditions were analyzed, identifying patterns indicative of motor health. The approach, leveraging synthetic data generation, demonstrates effectiveness in proactive maintenance and fault detection, reducing downtime and costs.
