Szczegóły publikacji
Opis bibliograficzny
Acoustic fault diagnosis of three-phase induction motors using smartphone and deep learning / Adam GŁOWACZ, Maciej Sulowicz, Jakub Zielonka, Zhixiong Li, Witold GŁOWACZ, Anil Kumar // Expert Systems with Applications ; ISSN 0957-4174. — 2025 — vol. 262 art. no. 125633, s. 1-8. — Bibliogr. s. 7-8, Abstr. — Publikacja dostępna online od: 2024-11-30
Autorzy (6)
- AGHGłowacz Adam
- Sulowicz Maciej
- Zielonka Jakub
- Li Zhixiong
- AGHGłowacz Witold
- Kumar Anil
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 156470 |
|---|---|
| Data dodania do BaDAP | 2024-12-13 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.eswa.2024.125633 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Expert Systems with Applications |
Abstract
Faults in induction motors can halt production lines in factories, leading to downtime and resulting in production and economic losses. Therefore, it is crucial to ensure that motors operate reliably. This paper describes an approach for the acoustic fault diagnosis of rotor bars in three-phase induction motors (IM). The authors analyzed the following conditions: a healthy IM, an IM with one broken rotor bar, an IM with two broken rotor bars, and an IM with three broken rotor bars. The FFT method was used to compute the FFT spectrum of the acoustic signals. An original feature extraction method DWV (Differences of Word Vectors) was proposed to compute the acoustic features. DenseNet-201, ResNet-18, ResNet-50, and EfficientNet-b0 were used to classify these acoustic features. The computed recognition efficiency is 100 %. The proposed method was also verified using a low-pass filter of 1–1225 Hz and word coding.
