Araştırma Makalesi
Asenkron motorlarda eşzamanlı kırık rotor çubukları ve statik eksenel kaçıklık arızalarının stator akımı ve titreşim sinyalleri analizi ile tespiti
Öz
Asenkron motorların endüstriyel üretim süreçlerinde yaygın olarak kullanılması, bu motorların arıza tespitlerinin doğru ve eksiksiz olarak yapılmasını gerekli kılmıştır. Kırık rotor çubukları ve eksenel kaçıklık arızaları asenkron motorlarda karşılaşılan arıza tiplerindendir. Bu iki arızanın aynı anda oluşması işletme ortamında sıklıkla karşılaşılan bir durumdur. Böyle anlarda, bu arızalara ait harmonik bileşenlerin net bir şekilde tespit edilebilmesi, planlanmamış işletme duruşlarının önlenmesi açısından önemlidir. Bu bileşenlerin tespitinde karşılaşılan önemli sorunların başında başka sinyaller (gürültü, baskın harmonik bileşenler vb.) içerisinde gizlenen düşük genlikli harmonik bileşenler gelmektedir. Bu çalışmada, eksenel kaçıklık ve kırık rotor çubuklarından oluşan çoklu arıza durumu incelenmiştir. Motorun akım ve titreşim sinyalleri dört farklı yük kademesinde (%25, %50, %75, %100) kaydedilerek, Hilbert zarf analizi yöntemi ile sinyal analizleri yapılmıştır. Analiz sonucunda karakteristik frekanslar ve bu bileşenlere ait genlikler zarf spektrumunda başarılı bir şekilde görüntülenebilmektedir. Grafiksel ve tablo formunda verilen sonuçlar, önerilen yöntemin aynı anda oluşan kırık rotor çubuğu ve eksenel kaçıklık arızalarının tespitinde kullanılabileceğini göstermektedir.
Anahtar Kelimeler
Destekleyen Kurum
TÜBİTAK (Türkiye Bilimsel ve Teknolojik Araştırma Kurumu)
Proje Numarası
116E302
Teşekkür
Çalışmalarımıza sunduğu destekten dolayı TÜBİTAK'a teşekkür ederiz.
Kaynakça
- 1. Irmak E., Vadi S., Computer based implementation of speed control experiment depending on frequency variation for induction motors, Journal of the Faculty of Engineering and Architecture of Gazi University, 26(1), 57-62, 2011.
- 2. Jiang C., Li S., Habetler T.G., A review of condition monitoring of induction motors based on stray flux, IEEE Energy Conversion Congress and Exposition, Cincinnati, OH, USA, 5424-5430, 1-5 October 2017.
- 3. Aydın İ., Karaköse M., Akın E., Negative selection based fuzzy fault diagnosis method, Journal of the Faculty of Engineering and Architecture of Gazi University, 24(4), 745-753, 2009.
- 4. Maruthi G.S., Hegde V., Application of MEMS acceloremeter for detection and diagnosis of multiple faults in the roller element bearings of three phase induction motor, IEEE Sensors Journal, 16(1), 145-152, 2016.
- 5. Ishkova I., Vitek O., Diagnosis of eccentricity and broken rotor bar related faults of induction motor by means of motor current signature analysis, 16th International Scientific Conference on Electric Power Engineering (EPE), Kouty nad Desnou, Czech Republic, 682-686, 20-22 May 2015.
- 6. Culbert I., Letal J., Signature analysis for online motor diagnostics: Early detection of rotating machine problems prior to failure, IEEE Industry Applications Magazine, 23(4), 76-81, 2017.
- 7. Soomro A.A., Kalwar I.H., Kazi K., Khoso S.K., Ansari S., A hybrid monitoring technique for diagnosis of mechanical faults in induction motor, Indian Journal of Science and Technology, 9(47), 1-9, 2016.
- 8. Ouachtouk I., Hani S.E., Dahi K., Wireless health monitoring system for rotor eccentricity faults detection in induction machine, Power Engineering and Electrical Engineering, 15(3), 376-382, 2017.
- 9. Supangat R., Grieger J., Ertugrul N., Soong W.L., Gray D.A., Hansen C., Investigation of static eccentricity fault frequencies using multiple sensors in induction motors and effects of loading, 32nd Annual Conference on IEEE Industrial Electronics, Paris, France, 958-963, 2006.
- 10. He Y.-L., Wang F.-L., Ke M.-Q., Tang G.-J., Rotor vibration difference among the single and the combined faults composed by static air-gap eccentricity and rotor interturn short circuit, Proceedings of the 9th IFToMM International Conference on Rotor Dynamics, Mechanisms and Machine Science, Vol. 21, Springer, Cham, 637-648, 2015.
- 11. Bayrak M., Küçüker A., A power based algorithm design for detection of broken rotor bars faults in three phase induction motors, Journal of the Faculty of Engineering and Architecture of Gazi University, 31(2), 303-311, 2016.
- 12. Rangel-Magdaleno, J., Peregrina-Barreto H., Ramirez-Cortes J, Cruz-Vega I., Hilbert spectrum analysis of induction motors for the detection of incipient broken rotor bars, Measurement, 109, 247-255, 2017.
- 13. Bessous N., Sbaa S., Toumi A., Experimental investigation on broken rotor bar faults in three phase induction motors using MVSA-FFT method, 6th International Conference on Control Engineering & Information Technology (CEIT), İstanbul, Turkey, 1-7, 25-27 October 2018.
- 14. Wang Z., Yang J., Li H., Zhen D., Xu Y., Gu F., Fault identification of broken rotor bars in induction motors using an improved cyclic modulation spectral analysis, Energies, 12(17), 3279, 2019.
- 15. Alham N.R., Asfani D.A., Negara I.M.Y., Dewantara B.Y., Analysis of load and unbalanced voltage on air gap eccentiricity in detection of three phase induction motor, International Conference on Information and Communications Technology, Yogyakarta, Indonesia, 566-571, 6-7 March 2018.
- 16. Kumar K.V., Raj A.C.B., Static eccentricity failure diagnosis for induction machine using wavelet analysis, International Conference on Innovations in Green Energy and Healthcare Technologies, Coimbatore, India, 1-5, 16-18 March 2017.
- 17. Lizarraga-Morales R.A., Rodriguez-Donate C., Cabal-Yepez E., Lopez-Ramirez M., Ledesma-Carrillo L.M., Ferrucho-Alvarez E., Novel FPGA-based methodology for early broken rotor detection and classification through homogeneity estimation, IEEE Transactions on Instrumentation and Measurement, 66(7), 1760-1769, 2017.
- 18. Gu F., Wang T., Alwodai A., Tian X., Shao Y., Ball A.D., A new method for accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals, Mechanical Systems and Signal Processing, 50-51, 400-413, 2015.
- 19. Sobra J., Kindl V., Skala B., Determination of the force caused by broken rotor bar and static eccentricity in an induction machine, ELEKTRO, Rajecke Teplice, Slovakia, 375-378, 19-20 May 2014.
- 20. Seghiour A., Seghier T., Zegnini B., Diagnostic of the simultaneous of dynamic eccentricity and broken rotor bars using the magnetic field spectrum of the air-gap for an induction machine, 3rd International Conference on Control, Engineering & Information Technology (CEIT), Tlemcen, Algeria, 1-6, 25-27 May 2015.
- 21. Mirzaeva G., Saad K.I., Advanced diagnosis of rotor faults and eccentricity in induction motors based on internal flux measurement, IEEE Transactions on Industry Applications, 54(3), 2981-2991, 2018.
- 22. Minaz M.R., Yıldız K., İndiksiyon motorun mekanik arıza teşhisinde makine öğrenme tekniklerinin kullanılması, European Journal of Science and Technology, 16, 881-904, 2019.
- 23. Tian S., Quian Z., Planetary gearbox fault feature enhancement based on comibined adaptive filter method, Advances in Mechanical Engineering, 7(12), 1-12, 2015.
- 24. Keshavamurthy T.G., Eshwarappa M.N., ECG signal de-noising based on adaptive filters, International Journal of Innovative Technology and Exploiring Engineering, 9(1), 5473-5483, 2019.
- 25. Jiang R., Liu S., Tang Y., Liu Y., A novel method of fault diagnosis for rolling element bearings based on the accumulated envelope spectrum of the wavelet packet, Journol of Vibration and Control, 21(8), 1580-1593, 2013.
- 26. Axelberg P.G.V., Bollen M., Gu I.Y.H., Trace of flicker sources by using the quantity of flicker power, IEEE Transactions on Power Delivery, 23(1), 465-471, 2008.
- 27. Bessam B., Menacer A., Boumehraz A., Cherif H., Detection of broken rotor bar faults in induction motor at low load using neural network, ISA Transactions 64, 241-246, 2016.
- 28. Elbouchikhi E., Choqueuse V., Amirat Y., Bembouzid M.E.H., Turri S., An efficient Hilbert-Huang transform-based bearing faults detection in induction machines, IEEE Transactions on Energy Conversion, 32(2), 401-413, 2017.
- 29. Ghanbari T., Autocorrelation function-based technique for stator turn-fault detection of induction motor, IET Science, Measurement & Technology, 10(2), 100-110, 2016.
- 30. Wang Z., Hilbert Transform applications in signal analysis and non-parametric identification of linear and nonlinear systems, Doctoral Dissertations, Missouri University of Science and Technology, Doctor of Philosophy in Civil Engineering, USA, 2011.
- 31. Gürsoy M.İ., Yılmaz A.S., Üstün S.V., Classification of power quality disturbances using Hilbert Huang transform and Gabor transform, Journal of Engineering Science of Adıyaman University, 4(7), 73-83, 2017.
- 32. Abd-el-Malek M., Abdelsalam A.K., Hassan O.E., Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert tarnsform, Mechanical Systems and Signal Processing, 93, 332-350, 2017.
- 33. Ünsal A., Güçlü S., Analysis of eccentric faults of induction motor by using Shannon entropy, Uludağ University Journal of the Faculty of Engineering, 24(3), 199-210, 2019.
Toplam 33 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Proje Numarası | 116E302 |
| Yayımlanma Tarihi | 2 Eylül 2021 |
| Gönderilme Tarihi | 4 Mart 2020 |
| Kabul Tarihi | 5 Nisan 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 36 Sayı: 4 |