Szczegóły publikacji
Opis bibliograficzny
Anomaly detection and diagnostic evaluation of DC motors in the context of PdM systems / Szymon PODLASEK, Paweł KNAP, Urszula Jachymczyk // W: 2025 26th International Carpathian Control Conference (ICCC) [Dokument elektroniczny] : 19-21 May 2025, Starý Smokovec, Slovakia : proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2025. — Dod. ISBN: 979-8-3315-0126-6, 979-8-3315-0128-0. — e-ISBN: 979-8-3315-0127-3. — S. [1–5]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [5], Abstr. — Publikacja dostępna online od: 2025-06-10
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 160880 |
|---|---|
| Data dodania do BaDAP | 2025-07-15 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ICCC65605.2025.11022915 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
The article presents research on fault detection in direct current (DC) motors within the framework of Predictive Maintenance (PdM) systems. The study aims to develop component of method for evaluating motor parameters, enabling the early identification of potential failures through the analysis of diagnostic signals. The approach combines advanced signal processing techniques with machine learning models to detect anomalies and assess the condition of various motor types.The applied methods include the analysis of accelerometric data collected under laboratory conditions. These data were used to identify deviations from normal operation. A comparative study of properly functioning and faulty motors was conducted to examine their impact on diagnostic characteristics. These techniques enabled a detailed assessment of motor conditions, revealing potential differences in the behavior of healthy and damaged units.The results obtained confirm the validity of the research and identification of anomalies to develop predictions of potential failures of different types of engines. The results emphasize the importance of employing PdM systems to optimize maintenance processes and reduce downtime risks. The research is a prelude to the work, which may also include analysis of electrical parameters, followed by determination of the type of damage. The article provides insights that support the further development of diagnostic technologies for machinery condition monitoring in Industry 4.0 concept.
