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Article Details

Article Details

  • Article Code : FIRAT-AKADEMI-501-5712
  • Article Type : Araştırma Makalesi
  • Publication Number : 2A0200
  • Page Number : 42-52
  • Doi : 10.12739/NWSA.2024.19.4.2A0200
  • Abstract Reading : 130
  • Download : 25
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Issue Details

  • Year : 2024
  • Volume : 19
  • Issue : 4
  • Number of Articles Published : 2
  • Published Date : 1.10.2024

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Dergi Kapağı
Technological Applied Sciences

Serial Number : 2A
ISSN No. : 1308-7223
Release Interval (in a Year) : 4 Issues

ASENKRON MOTORLARDA KIRIK ROTOR ÇUBUKLARININ ERKEN TEŞHİSİ: TRANSFER ÖĞRENME YAKLAŞIMLARININ DEĞERLENDİRİLMESİ

Özgür AYDIN1 , Erhan AKIN 2

Asenkron motorlar, basit tasarımları, düşük bakım gereksinimleri ve uzun ömürleri nedeniyle hem günlük hayatta hem de endüstriyel uygulamalarda geniş bir kullanım alanına sahiptir. Bu motorlarda meydana gelen rotor arızalarının hızlı ve güvenilir bir şekilde tespit edilmesi, işletmelerin verimliliği ve sürekliliği açısından kritik öneme sahiptir. Bununla birlikte, transfer öğrenme yöntemi, motor arızalarının teşhisinde hala yeterince araştırılmamış bir alan olarak dikkat çekmektedir. Transfer öğrenme, mevcut veri setlerinden elde edilen bilgi birikimini yeni problemlere uyarlayarak, asenkron motorlarda rotor çubuğu arızalarının daha güvenilir bir şekilde teşhis edilmesini sağlayabilir. Bu çalışma, kırık rotor çubuklarının erken tespiti için transfer öğrenme modellerinin etkinliğini incelemektedir. Araştırmada, hazır bir veri setinden elde edilen beş farklı zaman-frekans görüntüsü kullanılmıştır. Bu görüntüler üzerinde beş farklı transfer öğrenme modeli değerlendirilmiş ve performansları karşılaştırılmıştır. Sonuçlar, transfer öğrenme yaklaşımlarının asenkron motor arızalarının teşhisinde %99,5'in üzerinde doğruluk oranlarına ulaştığını göstermektedir. Bu bulgular, yöntemin motor arıza teşhisi için etkili ve umut verici bir çözüm sunduğunu ortaya koymaktadır.

Keywords
Asenkron Motor, Arıza Teşhisi, Kırık Rotor Çubuğu, Transfer Öğrenme, Görüntü İşleme,

EARLY DETECTION OF BROKEN ROTOR BARS IN INDUCTION MOTORS: EVALUATION OF TRANSFER LEARNING APPROACHES

Özgür AYDIN1 , Erhan AKIN 3

Induction motors are widely used in both daily life and industrial applications due to their simple design, low maintenance requirements, and long service life. The rapid and reliable detection of rotor faults in these motors is critically important for ensuring operational efficiency and continuity in businesses. However, transfer learning remains an underexplored area in the diagnosis of motor faults. Transfer learning enables the application of knowledge gained from existing datasets to new problems, providing a more reliable approach for detecting rotor bar faults in induction motors. This study investigates the effectiveness of transfer learning models for the early detection of broken rotor bars. The research utilizes five different time-frequency images obtained from a pre-existing dataset. These images are analyzed using five distinct transfer learning models, and their performances are compared. The results demonstrate that transfer learning approaches achieve an accuracy rate exceeding 99.5% in diagnosing induction motor faults. These findings highlight the potential of this method as an effective and promising solution for motor fault diagnosis.

Keywords
Induction Motor, Fault Diagnosis, Broken Rotor Bar, Transfer Learning, Image Processing,

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Authors

Özgür AYDIN (1) (Corresponding Author)
Bingöl Üniversitesi - Enformatik Bölüm Başkanlığı
iamozguraydin@gmail.com | 0000-0001-8130-277X

Erhan AKIN (2)

Fırat Üniversitesi
eakin@firat.edu.tr | 0000-0001-6476-9255

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References
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