Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Rotor fault characterization study by considering normalization analysis, feature extraction, and a multi-class classifier
Engineering Research Express · Haziran 2024
YÖKSİS Kayıtları
Rotor fault characterization study by considering normalization analysis, feature extraction, and a multi-class classifier
Engineering Research Express · 2024 ESCI
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Makale Bilgileri
DergiEngineering Research Express
Yayın TarihiHaziran 2024
Cilt / Sayfa6
Scopus ID2-s2.0-85189859090
Erişim🔓 Açık Erişim
Özet
Background. Rotor faults are the most common malfunctions encountered, especially during the manufacturing stage, in asynchronous motors. These faults cause vibration in the motor torque and a decrease in efficiency. In recent years, the detection of rotor faults has been done using motor current. The reflection of rotor faults on motor current depends on slip, and therefore, the effect increases as the current grows. Good results are achieved in fault detection at nominal loads. However, especially when motor manufacturers are considered, testing the motor by loading it requires expensive testing equipment and long-term test procedures. Therefore, the detection of faults in the motor at no load is emphasized. However, since the effect of the fault decreases when the motor is at no load, fault detection becomes difficult. Generally, small-level faults cannot be detected. Objective. This study focuses on fault detection from the motor current at no load. The development current at no load was used to eliminate the negative effects of slip. However, since the slip is not constant, the change in frequency and amplitude values to be used as a feature makes the diagnosis difficult. Method. In this study, the spectrogram was used to evaluate the change during the start-up time. Thus, a standard dataset was determined for comparison. The texture properties of the spectrogram image were extracted using various methods. The extracted features were subjected to normalization analysis and classified using the k-NN algorithm. Results. In the classification phase, a classification accuracy of 98.66% was achieved using the k-NN method, and it was seen that the proposed method could be used for the detection of rotor faults. Conclusions. The study has successfully demonstrated that broken rotor bar faults in asynchronous motors can be diagnosed using the motor start-up data.
Yazarlar (2)
1
Mücahid Barstuğan
ORCID: 0000-0001-9790-5890
2
Hayri Arabaci
Anahtar Kelimeler
asynchronous motor
classification
feature extraction
rotor fault
spectrogram
Kurumlar
Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
1
Atıf
2
Yazar
5
Anahtar Kelime