Szczegóły publikacji
Opis bibliograficzny
Fault diagnosis of electrical faults of three-phase induction motors using acoustic analysis / Adam Głowacz, Maciej Sulowicz, Jarosław KOZIK, Krzysztof PIECH, Witold GŁOWACZ, Zhixiong Li, Frantisek Brumercik, Miroslav Gutten, Daniel Korenciak, Anil Kumar, Guilherme Beraldi Lucas, Muhammad Irfan, Wahyu Caesarendra, Hui Liu // Bulletin of the Polish Academy of Sciences . Technical Sciences ; ISSN 0239-7528. — 2024 — vol. 72 no. 1 art. no. e148440, s. 1-7. — Bibliogr. s. 6-7, Abstr. — Publikacja dostępna online od: 2024-01-30. — A. Głowacz – afiliacja: Cracow University of Technology
Autorzy (14)
- Głowacz Adam
- Sulowicz Maciej
- AGHKozik Jarosław
- AGHPiech Krzysztof
- AGHGłowacz Witold
- Li Zhixiong
- Brumercik Frantisek
- Gutten Miroslav
- Korenciak Daniel
- Kumar Anil
- Lucas Guilherme Beraldi
- Irfan Muhammad
- Caesarendra Wahyu
- Liu Hui
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 152296 |
|---|---|
| Data dodania do BaDAP | 2024-04-12 |
| Tekst źródłowy | URL |
| DOI | 10.24425/bpasts.2024.148440 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Bulletin of the Polish Academy of Sciences, Technical Sciences |
Abstract
Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with two broken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using fast Fourier transform (FFT), a new feature extraction method called MoD-7 (maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications.
