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Asenkron Motor Rulman Hatalarının Uzun-Kısa Süreli Bellek Tipi Derin Sinir Ağları ile Otomatik Sınıflandırılması

Yıl 2021, Sayı: 32, 508 - 513, 31.12.2021
https://doi.org/10.31590/ejosat.1039836

Öz

Endüstride yaygın olarak kullanılan asenkron motorların tercih edilmesinin nedenleri hesaplı, dayanıklı ve güvenilir olmalarıdır. Asenkron motorların iç bilezik, bilye ve dış bilezik kısımlarımda oluşan rulman hataları en sık karşılaşılan hatalardandır. Bu nedenle, asenkron motorlarının çalışmasının verimini arttırmak için rulman hatalarının erken bir aşamada belirlenmesi oldukça önemlidir. Bu çalışmada, Case Western Reserve University (CWRU) rulman veriseti kullanılarak, asenkron motor rulmanlarının iç bilezik, dış bilezik ve bilye bölgelerinde oluşan hataların titreşim verilerinden yararlanarak otomatik sınıflandırılması için iki yönlü uzun-kısa süreli bellek tipi (IY-UKSB) tipi derin sinir ağları tabanlı bir yöntem önerilmektedir. Çalışmada, normal rulman ve hatalı rulmana ait titreşim verileri 128, 256, 512 ve 1024 gibi farklı boyutlarda pencerelere ayrılarak, anlık ferekans ve sprektral entropi ile özellik çıkarımı sonucunda önerilen IY-UKSB ağının performansı değerlendirilmiştir. Çalışmada normal ve hatalı rulman verilerinden oluşturulan veriseti üzerinde farklı pencere genişliklerinde test kümesi üzerinde IY-UKSB ağının doğruluğunun ortalama %80 civarında kaldığı, buna karşın normal ve hatalı rulman verilerinin sınıflandırılmasında anlık frekans ve spektral entropi ile özellik çıkarımı sonrası IY-UKSB ağının ortalama %99.28 doğruluk, %99.72 duyarlılık ve %97.53 seçicilik skorlarına ulaştığı görülmüştür. Sonuç olarak, önerilen IY-UKSB ağının hatalı ve normal rulman titreşim verilerinin ayrımı için güçlü bir sınıflandırıcı olduğu değerlendirilmiştir.

Kaynakça

  • Akkurt, İ., & Arabacı, H. (2019). Sürücüden Beslenen Asenkron Motorlarda Rulman Arızalarının Stator Akımı Kullanarak Tespiti. Uluslararası Doğu Anadolu Fen Mühendislik ve Tasarım Dergisi, 1(2), 122-134.
  • Al-Musawi, A. K., Anayi, F., & Packianather, M. (2020). Three-phase induction motor fault detection based on thermal image segmentation. Infrared Physics & Technology, 104, 103140.
  • Amar, M., Gondal, I., & Wilson, C. (2014). Vibration spectrum imaging: A novel bearing fault classification approach. IEEE transactions on Industrial Electronics, 62(1), 494-502.
  • Bayram, S., Kaplan, K., Kuncan, M., & Ertunç, H. M. (2013, 26-28 Eylül 2013). Bilyeli rulmanlarda zaman uzayında istatistiksel öznitelik çıkarımı ve yapay sinir ağları metodu ile hata boyutunun kestirimi. Paper presented at the Otomatik Kontrol Ulusal Toplantıs (TOK2013), Malatya. pp. 986-991.
  • Benbouzid, M. (1999). Bibliography on induction motors faults detection and diagnosis. IEEE Transactions on Energy Conversion, 14(4), 1065-1074.
  • Benbouzid, M., & Kliman, G. B. (2003). What stator current processing-based technique to use for induction motor rotor faults diagnosis? IEEE Transactions on Energy Conversion, 18(2), 238-244.
  • Chen, X., Zhang, B., & Gao, D. (2021). Bearing fault diagnosis base on multi-scale CNN and LSTM model. Journal of Intelligent Manufacturing, 32, 971-987.
  • CWRU. (2021). Case Western Reserve University Bearing Data Center. Available online: https://engineering.case.edu/bearingdatacenter
  • Çalış, H., Cakir, A., & Dandil, E. (2013). Artificial immunity-based induction motor bearing fault diagnosis. Turkish Journal of Electrical Engineering and Computer Science, 21(1), 1-25.
  • Dandıl, E., & Karaca, S. (2020). MR Spektroskopi Sinyalleri Kullanılarak LSTM Derin Sinir Ağları ile Beyinde Sahte Tümörlerin Tespiti. Avrupa Bilim ve Teknoloji Dergisi, 426-433.
  • Demir, H. G., & Müştak, O. (2021). Rulman Hasarlarının Titreşim ve Gürültü Analizi ile Tespiti. Avrupa Bilim ve Teknoloji Dergisi(25), 571-581.
  • Eren, L., Ince, T., & Kiranyaz, S. (2019). A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. Journal of Signal Processing Systems, 91(2), 179-189.
  • Graves, A., Mohamed, A.-r., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. Paper presented at the 2013 IEEE international conference on acoustics, speech and signal processing. pp. 6645-6649.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • Hwang, D.-H., Youn, Y.-W., Sun, J.-H., Choi, K.-H., Lee, J.-H., & Kim, Y.-H. (2015). Support vector machine based bearing fault diagnosis for induction motors using vibration signals. Journal of Electrical Engineering and Technology, 10(4), 1558-1565.
  • Immovilli, F., Lippi, M., & Cocconcelli, M. (2019). Automated bearing fault detection via long short-term memory networks. Paper presented at the 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). pp. 452-458.
  • Jalayer, M., Orsenigo, C., & Vercellis, C. (2021). Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms. Computers in Industry, 125, 103378.
  • Kompella, K. D., Mannam, V. G. R., & Rayapudi, S. R. (2016). DWT based bearing fault detection in induction motor using noise cancellation. Journal of Electrical Systems and Information Technology, 3(3), 411-427.
  • Konar, P., & Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 11(6), 4203-4211.
  • Mendel, E., Rauber, T. W., Varejão, F. M., & Batista, R. J. (2009). Rolling element bearing fault diagnosis in rotating machines of oil extraction rigs. Paper presented at the 2009 17th European Signal Processing Conference. pp. 1602-1606.
  • Sak, H., Senior, A. W., & Beaufays, F. (2014). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Available online: https://storage.googleapis.com/pub-tools-public-publication-data/pdf/43905.pdf
  • Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE transactions on Signal Processing, 45(11), 2673-2681.
  • Stuner, B., Chatelain, C., & Paquet, T. (2020). Handwriting recognition using cohort of LSTM and lexicon verification with extremely large lexicon. Multimedia Tools and Applications, 79(45), 34407-34427.
  • Toma, R. N., Prosvirin, A. E., & Kim, J.-M. (2020). Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, 20(7), 1884.
  • Ünsal, A., & Karakaya, O. (2015). ASENKRON MOTOR ROTOR ARIZALARININ ANALİZİ. Journal of Science and Technology of Dumlupınar University(034), 69-86.
  • Yeh, C.-C., Sizov, G. Y., Sayed-Ahmed, A., Demerdash, N. A., Povinelli, R. J., Yaz, E. E., & Ionel, D. M. (2008). A reconfigurable motor for experimental emulation of stator winding interturn and broken bar faults in polyphase induction machines. IEEE Transactions on Energy Conversion, 23(4), 1005-1014.
  • Yeşilyurt, İ., & Özdemir, Ö. (2015). Dengesiz Yüke Maruz Silindirik Masuralı Rulman Arızasının Kısa Zamanlı Fourier Dönüşümü Yardımıyla Belirlenmesi. Uluslararası Katılımlı 17. Makina Teorisi Sempozyumu, 17, 1-8.
  • Zarei, J. (2012). Induction motors bearing fault detection using pattern recognition techniques. Expert systems with Applications, 39(1), 68-73.
  • Zarei, J., Tajeddini, M. A., & Karimi, H. R. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 24(2), 151-157.

Automatic Classification of Induction Motor Bearing Faults using Long-Short Term Memory Deep Neural Networks

Yıl 2021, Sayı: 32, 508 - 513, 31.12.2021
https://doi.org/10.31590/ejosat.1039836

Öz

The reasons for the preference of induction motors, which are widely used in the industry, are that they are affordable, durable and reliable. Bearing errors in the inner race, ball and outer race parts of induction motors are the most common errors. Therefore, it is very important to detect bearing faults at an early stage in order to increase the efficiency of operation of induction motors. In this study, using Case Western Reserve University (CWRU) bearing dataset, bi-directional long-short-term memory type (Bi-LSTM) deep neural networks are proposed for automatic classification of faults in the inner race, outer race and ball regions of induction motor bearings on vibration data. In the study, the performance of the proposed Bi-LSTM network is evaluated as a result of feature extraction using instantaneous frequency and spectral entropy, by dividing the vibration data of normal bearing and faulty bearing into windows of different sizes such as 128, 256, 512 and 1024. In the study, the accuracy of the Bi-LSTM network for the test set with different window widths on the dataset created from normal and faulty bearing data is 80% on average, on the other hand, after feature extraction with instantaneous frequency and spectral entropy in the classification of normal and faulty bearing data, the accuracy of Bi-LSTM network is observed 99.28% accuracy, 99.72% sensitivity and 97.53% specifity scores. As a result, the proposed Bi-LSTM network is considered to be a powerful classifier for the separation of faulty and normal bearing vibration data.

Kaynakça

  • Akkurt, İ., & Arabacı, H. (2019). Sürücüden Beslenen Asenkron Motorlarda Rulman Arızalarının Stator Akımı Kullanarak Tespiti. Uluslararası Doğu Anadolu Fen Mühendislik ve Tasarım Dergisi, 1(2), 122-134.
  • Al-Musawi, A. K., Anayi, F., & Packianather, M. (2020). Three-phase induction motor fault detection based on thermal image segmentation. Infrared Physics & Technology, 104, 103140.
  • Amar, M., Gondal, I., & Wilson, C. (2014). Vibration spectrum imaging: A novel bearing fault classification approach. IEEE transactions on Industrial Electronics, 62(1), 494-502.
  • Bayram, S., Kaplan, K., Kuncan, M., & Ertunç, H. M. (2013, 26-28 Eylül 2013). Bilyeli rulmanlarda zaman uzayında istatistiksel öznitelik çıkarımı ve yapay sinir ağları metodu ile hata boyutunun kestirimi. Paper presented at the Otomatik Kontrol Ulusal Toplantıs (TOK2013), Malatya. pp. 986-991.
  • Benbouzid, M. (1999). Bibliography on induction motors faults detection and diagnosis. IEEE Transactions on Energy Conversion, 14(4), 1065-1074.
  • Benbouzid, M., & Kliman, G. B. (2003). What stator current processing-based technique to use for induction motor rotor faults diagnosis? IEEE Transactions on Energy Conversion, 18(2), 238-244.
  • Chen, X., Zhang, B., & Gao, D. (2021). Bearing fault diagnosis base on multi-scale CNN and LSTM model. Journal of Intelligent Manufacturing, 32, 971-987.
  • CWRU. (2021). Case Western Reserve University Bearing Data Center. Available online: https://engineering.case.edu/bearingdatacenter
  • Çalış, H., Cakir, A., & Dandil, E. (2013). Artificial immunity-based induction motor bearing fault diagnosis. Turkish Journal of Electrical Engineering and Computer Science, 21(1), 1-25.
  • Dandıl, E., & Karaca, S. (2020). MR Spektroskopi Sinyalleri Kullanılarak LSTM Derin Sinir Ağları ile Beyinde Sahte Tümörlerin Tespiti. Avrupa Bilim ve Teknoloji Dergisi, 426-433.
  • Demir, H. G., & Müştak, O. (2021). Rulman Hasarlarının Titreşim ve Gürültü Analizi ile Tespiti. Avrupa Bilim ve Teknoloji Dergisi(25), 571-581.
  • Eren, L., Ince, T., & Kiranyaz, S. (2019). A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. Journal of Signal Processing Systems, 91(2), 179-189.
  • Graves, A., Mohamed, A.-r., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. Paper presented at the 2013 IEEE international conference on acoustics, speech and signal processing. pp. 6645-6649.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • Hwang, D.-H., Youn, Y.-W., Sun, J.-H., Choi, K.-H., Lee, J.-H., & Kim, Y.-H. (2015). Support vector machine based bearing fault diagnosis for induction motors using vibration signals. Journal of Electrical Engineering and Technology, 10(4), 1558-1565.
  • Immovilli, F., Lippi, M., & Cocconcelli, M. (2019). Automated bearing fault detection via long short-term memory networks. Paper presented at the 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). pp. 452-458.
  • Jalayer, M., Orsenigo, C., & Vercellis, C. (2021). Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms. Computers in Industry, 125, 103378.
  • Kompella, K. D., Mannam, V. G. R., & Rayapudi, S. R. (2016). DWT based bearing fault detection in induction motor using noise cancellation. Journal of Electrical Systems and Information Technology, 3(3), 411-427.
  • Konar, P., & Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 11(6), 4203-4211.
  • Mendel, E., Rauber, T. W., Varejão, F. M., & Batista, R. J. (2009). Rolling element bearing fault diagnosis in rotating machines of oil extraction rigs. Paper presented at the 2009 17th European Signal Processing Conference. pp. 1602-1606.
  • Sak, H., Senior, A. W., & Beaufays, F. (2014). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Available online: https://storage.googleapis.com/pub-tools-public-publication-data/pdf/43905.pdf
  • Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE transactions on Signal Processing, 45(11), 2673-2681.
  • Stuner, B., Chatelain, C., & Paquet, T. (2020). Handwriting recognition using cohort of LSTM and lexicon verification with extremely large lexicon. Multimedia Tools and Applications, 79(45), 34407-34427.
  • Toma, R. N., Prosvirin, A. E., & Kim, J.-M. (2020). Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, 20(7), 1884.
  • Ünsal, A., & Karakaya, O. (2015). ASENKRON MOTOR ROTOR ARIZALARININ ANALİZİ. Journal of Science and Technology of Dumlupınar University(034), 69-86.
  • Yeh, C.-C., Sizov, G. Y., Sayed-Ahmed, A., Demerdash, N. A., Povinelli, R. J., Yaz, E. E., & Ionel, D. M. (2008). A reconfigurable motor for experimental emulation of stator winding interturn and broken bar faults in polyphase induction machines. IEEE Transactions on Energy Conversion, 23(4), 1005-1014.
  • Yeşilyurt, İ., & Özdemir, Ö. (2015). Dengesiz Yüke Maruz Silindirik Masuralı Rulman Arızasının Kısa Zamanlı Fourier Dönüşümü Yardımıyla Belirlenmesi. Uluslararası Katılımlı 17. Makina Teorisi Sempozyumu, 17, 1-8.
  • Zarei, J. (2012). Induction motors bearing fault detection using pattern recognition techniques. Expert systems with Applications, 39(1), 68-73.
  • Zarei, J., Tajeddini, M. A., & Karimi, H. R. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 24(2), 151-157.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Rumeysa Hacer Kılıç Bu kişi benim 0000-0002-0768-9133

Emre Dandıl 0000-0001-6559-1399

Yayımlanma Tarihi 31 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 32

Kaynak Göster

APA Kılıç, R. H., & Dandıl, E. (2021). Asenkron Motor Rulman Hatalarının Uzun-Kısa Süreli Bellek Tipi Derin Sinir Ağları ile Otomatik Sınıflandırılması. Avrupa Bilim Ve Teknoloji Dergisi(32), 508-513. https://doi.org/10.31590/ejosat.1039836