BibTex RIS Kaynak Göster

Induction Motor Fault Detection Using Current Space Pattern Recognition

Yıl 2018, Cilt: 7 Sayı: 2, 88 - 94, 01.07.2018

Öz

Over the last decade, continuous monitoring of complex dynamic systems has become an increasingly important issue in various engineering disciplines. In this study, a model recognition based system using visual based efficient in variants is presented for continuous monitoring of induction motors. The steps described in this article are based on the image description of the 3-dimensional state space patterns, which allows identification of different types of faults and also their corresponding fault severity. This automatic fault detection system deals with time-varying motor currents and is based on the identification of the specified three-phase stator currents. Various simulations results are also presented to confirm the effectiveness of the proposed method

Kaynakça

  • [1] O. V. Thorsen and M. Dalva, “A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals and oil refineries,” in Proceedings of IEEE Petroleum and Chemical Industry Technical Conference (PCIC ’94), pp. 1–9.
  • [2] P. . J. Tavner, “Review of condition monitoring of rotating electrical machines,” IET Electr. Power Appl., vol. 2, no. 4, pp. 215–247, 2008.
  • [3] W. T. Thomson and M. Fenger, “Current signature analysis to detect induction motor faults,” IEEE Ind. Appl. Mag., vol. 7, no. 4, pp. 26–34, 2001.
  • [4] G. B. Kliman, R. A. Koegl, J. Stein, R. D. Endicott, and M. W. Madden, “Noninvasive detection of broken rotor bars in operating induction motors,” IEEE Trans. Energy Convers., vol. 3, no. 4, pp. 873–879, 1988.
  • [5] Ye Zhongming and Wu Bin, “A review on induction motor online fault diagnosis,” in Proceedings IPEMC 2000. Third International Power Electronics and Motion Control Conference (IEEE Cat. No.00EX435), 2000, vol. 3, pp. 1353–1358.
  • [6] W. T. Thomson and I. Culbert, Current Signature Analysis for Condition Monitoring of Cage Induction Motors : Industrial Application and Case Histories. .
  • [7] M. El Hachemi Benbouzid, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 984–993, 2000.
  • [8] H. A. Toliyat, S. P. Waikar, and T. A. Lipo, “Analysis and simulation of five-phase synchronous reluctance machines including third harmonic of airgap MMF,” IEEE Trans. Ind. Appl., vol. 34, no. 2, pp. 332–339, 1998.
  • [9] M. Wolkiewicz and C. T. Kowalski, “On-line neural network-based stator fault diagnosis system of the converter-fed induction motor drive,” in IECON Proceedings (Industrial
  • [10] R. M. Tallam et al., “A Survey of Methods for Detection of Stator-Related Faults in Induction Machines,” IEEE Trans. Ind. Appl., vol. 43, no. 4, pp. 920–933, 2007.
  • [11] Q. Wu and S. Nandi, “Fast single-turn sensitive stator interturn fault detection of induction machines based on positiveand negative-sequence third harmonic components of line currents,” in IEEE Transactions on Industry Applications, 2010, vol. 46, no. 3, pp. 974–983.
  • [12] A. M. Trzynadlowski and E. Ritchie, “Comparative investigation of diagnostic media for induction motors: a case of rotor cage faults,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 1092–1099, 2000.
  • [13] P. Carbonetto, G. Dorkó, C. Schmid, H. Kück, and N. De Freitas, “Learning to recognize objects with little supervision,” Int. J. Comput. Vis., vol. 77, no. 1–3, pp. 219–237, 2008.
  • [14] O. Gloger, M. Ehrhardt, T. Dietrich, O. Hellwich, K. Graf, and E. Nagel, “A threestepped coordinated level set segmentation method for identifying atherosclerotic plaques on MR-images,” Commun. Numer. Methods Eng., vol. 25, no. 6, pp. 615–638, 2009.
  • [15] S. Wang, F. lai Chung, and F. Xiong, “A novel image thresholding method based on Parzen window estimate,” Pattern Recognit., vol. 41, no. 1, pp. 117–129, 2008.
  • [16] Y. J. Zhang, “Influence of segmentation over feature measurement,” Pattern Recognit. Lett., vol. 16, no. 2, pp. 201–206, 1995.

Asenkron Motor Arıza Tespitinde Akım Uzay Örüntü Tanıma Sisteminin Kullanılması

Yıl 2018, Cilt: 7 Sayı: 2, 88 - 94, 01.07.2018

Öz

Son on yılda, karmaşık dinamik sistemlerin sürekli olarak izlenmesi, çeşitli mühendislik alanlarında giderek daha önemli bir konu haline gelmiştir. Bu çalışmada, asenkron motorların sürekli izlenmesi için görsel tabanlı verimli invaryantlar kullanan bir model tanıma tabanlı sistem sunulmaktadır. Bu makalede anlatılan aşamalar, farklı arıza tiplerinin tanımlanmasına ve ayrıca bunlara karşılık gelen arıza şiddetinin belirlenmesine izin veren 3-boyutlu durum uzay örüntülerinin görüntü kompozisyonuna dayanmaktadır. Bu otomatik arıza tespit sistemi, zamanla değişenmotor akımları ile ilgilenir ve belirtilen üç-fazlı stator akımlarının tanımlanmasına dayanır. Önerilen metodun etkinliğini doğrulamak amacıylabenzetim sonuçları da sunulmuştur

Kaynakça

  • [1] O. V. Thorsen and M. Dalva, “A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals and oil refineries,” in Proceedings of IEEE Petroleum and Chemical Industry Technical Conference (PCIC ’94), pp. 1–9.
  • [2] P. . J. Tavner, “Review of condition monitoring of rotating electrical machines,” IET Electr. Power Appl., vol. 2, no. 4, pp. 215–247, 2008.
  • [3] W. T. Thomson and M. Fenger, “Current signature analysis to detect induction motor faults,” IEEE Ind. Appl. Mag., vol. 7, no. 4, pp. 26–34, 2001.
  • [4] G. B. Kliman, R. A. Koegl, J. Stein, R. D. Endicott, and M. W. Madden, “Noninvasive detection of broken rotor bars in operating induction motors,” IEEE Trans. Energy Convers., vol. 3, no. 4, pp. 873–879, 1988.
  • [5] Ye Zhongming and Wu Bin, “A review on induction motor online fault diagnosis,” in Proceedings IPEMC 2000. Third International Power Electronics and Motion Control Conference (IEEE Cat. No.00EX435), 2000, vol. 3, pp. 1353–1358.
  • [6] W. T. Thomson and I. Culbert, Current Signature Analysis for Condition Monitoring of Cage Induction Motors : Industrial Application and Case Histories. .
  • [7] M. El Hachemi Benbouzid, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 984–993, 2000.
  • [8] H. A. Toliyat, S. P. Waikar, and T. A. Lipo, “Analysis and simulation of five-phase synchronous reluctance machines including third harmonic of airgap MMF,” IEEE Trans. Ind. Appl., vol. 34, no. 2, pp. 332–339, 1998.
  • [9] M. Wolkiewicz and C. T. Kowalski, “On-line neural network-based stator fault diagnosis system of the converter-fed induction motor drive,” in IECON Proceedings (Industrial
  • [10] R. M. Tallam et al., “A Survey of Methods for Detection of Stator-Related Faults in Induction Machines,” IEEE Trans. Ind. Appl., vol. 43, no. 4, pp. 920–933, 2007.
  • [11] Q. Wu and S. Nandi, “Fast single-turn sensitive stator interturn fault detection of induction machines based on positiveand negative-sequence third harmonic components of line currents,” in IEEE Transactions on Industry Applications, 2010, vol. 46, no. 3, pp. 974–983.
  • [12] A. M. Trzynadlowski and E. Ritchie, “Comparative investigation of diagnostic media for induction motors: a case of rotor cage faults,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 1092–1099, 2000.
  • [13] P. Carbonetto, G. Dorkó, C. Schmid, H. Kück, and N. De Freitas, “Learning to recognize objects with little supervision,” Int. J. Comput. Vis., vol. 77, no. 1–3, pp. 219–237, 2008.
  • [14] O. Gloger, M. Ehrhardt, T. Dietrich, O. Hellwich, K. Graf, and E. Nagel, “A threestepped coordinated level set segmentation method for identifying atherosclerotic plaques on MR-images,” Commun. Numer. Methods Eng., vol. 25, no. 6, pp. 615–638, 2009.
  • [15] S. Wang, F. lai Chung, and F. Xiong, “A novel image thresholding method based on Parzen window estimate,” Pattern Recognit., vol. 41, no. 1, pp. 117–129, 2008.
  • [16] Y. J. Zhang, “Influence of segmentation over feature measurement,” Pattern Recognit. Lett., vol. 16, no. 2, pp. 201–206, 1995.
Toplam 16 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Research Article
Yazarlar

Ferhat Çıra Bu kişi benim

Yayımlanma Tarihi 1 Temmuz 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 7 Sayı: 2

Kaynak Göster

IEEE F. Çıra, “Asenkron Motor Arıza Tespitinde Akım Uzay Örüntü Tanıma Sisteminin Kullanılması”, DÜFED, c. 7, sy. 2, ss. 88–94, 2018.


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