DATA FUSION BASED MULTIMODAL FAULT DIAGNOSIS IN PERMANENT MAGNET SYNCHRONOUS MOTORS

Merve Cömert; Evin Şahin Sadık; Abdurrahman Ünsal

doi:10.17780/ksujes.1723915

EN TR

DATA FUSION BASED MULTIMODAL FAULT DIAGNOSIS IN PERMANENT MAGNET SYNCHRONOUS MOTORS

Abstract

In this study, a deep learning-based method is proposed for the classification of inter-turn and inter-coil short circuit faults occurring in three-phase Permanent Magnet Synchronous Motors (PMSM). Three-phase current and vibration signals are used by multi-mode data fusion and a Convolutional Neural Networks (CNN) model. The spectrograms used as input in the CNN model are obtained using Short Time Fourier Transform (STFT). Using the proposed method, faults are classified with high accuracies (inter-turn faults with 100% accuracy, inter-coil faults with 98.95% accuracy). The obtained results show that the proposed multi-mode fusion approach provides high success in both fault detection and fault severity classification.

Keywords

DATA FÜZYON TABANLI ÇOK MODLU YÖNTEM İLE SABİT MIKNATISLI SENKRON MOTOR ARIZALARININ TESPİTİ

Öz

Bu çalışmada, üç fazlı Sabit Mıknatıslı Senkron Motorlarda (PMSM) meydana gelen iki temel stator arızası olan sarımlar arası (inter-turn) ve bobinler arası (inter-coil) kısa devre arızalarının sınıflandırılmasına yönelik derin öğrenme tabanlı bir yöntem önerilmiştir. Üç faz akım ve titreşim sinyalleri çok modlu veri füzyonu ve Evrişimli Sinir Ağları (CNN) ile kullanılarak sınıflandırma yapılmıştır. CNN modelinde girdi olarak kullanılan spektrogramlar Kısa Süreli Fourier Dönüşümü (STFT) kullanılarak elde edilmiştir. Önerilen yöntem ile arıza şiddetleri yüksek doğruluk oranı (inter-turn arızaları %100 doğrulukla, inter-coil arızaları %98.95 doğrulukla) ile sınıflandırılmıştır. Elde edilen sonuçlar, önerilen çok modlu füzyon yaklaşımının hem arıza tespitinde hem de arıza şiddeti sınıflandırılmasında yüksek başarı sunduğunu ortaya koymaktadır.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Elektrik Makineleri ve Sürücüler

Bölüm

Araştırma Makalesi

Yazarlar

Merve Cömert
0000-0001-2345-6789
Türkiye

Evin Şahin Sadık ^*
0000-0002-2212-4210
Türkiye

Abdurrahman Ünsal
0000-0002-7053-517X
Türkiye

Yayımlanma Tarihi

3 Eylül 2025

Gönderilme Tarihi

20 Haziran 2025

Kabul Tarihi

13 Temmuz 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 3

DOI

https://doi.org/10.17780/ksujes.1723915

IZ

https://izlik.org/JA36WX88KG

Kaynak Göster

RIS / Bibtex

APA

Cömert, M., Şahin Sadık, E., & Ünsal, A. (2025). DATA FUSION BASED MULTIMODAL FAULT DIAGNOSIS IN PERMANENT MAGNET SYNCHRONOUS MOTORS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1546-1557. https://doi.org/10.17780/ksujes.1723915

Cited By

Fault Detection and Diagnosis for Multi‐Faults of PMSM‐Drive Systems Using a Hybrid Machine Learning Method

IET Power Electronics

https://doi.org/10.1049/pel2.70203