| Autor: |
Adam Glowacz, Maciej Sulowicz, Jarosław Kozik, Krzysztof Piech, Witold Glowacz, Zhixiong Li, Frantisek Brumercik, Miroslav Gutten, Daniel Korenciak, Anil Kumar, Guilherme Beraldi Lucas, Muhammad Irfan, Wahyu Caesarendra, Hui Lui |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol 72, Iss 1 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2300-1917 |
| DOI: |
10.24425/bpasts.2024.148440 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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