Asenkron Motor Rotor Arızalarının İstatiksel Analiz Yöntemi ile Değerlendirilmesi

Bu makalede sincap kafesli asenkron motorlarda yaygın olarak meydana gelen rotor arızalarının istatistiksel olarak analizi ve değerlendirilmesi yapılacaktır. Rotor arızaları genel olarak rotor çubuklarının kırılması ve kısa-devre halkasının çatlaması sonucu meydana gelir. Rotor arızaları stator akımına harmonik olarak yansımaktadırlar. Bu harmoniklerin tespiti için çeşitli sinyal işleme yöntemleri kullanılmaktır. Bu harmonikler ve diğer özellik çıkarım yöntemleri asenkron motor arızalarını tespit edilmesi için kullanılmaktadır. Bu çalışmada stator akımı önce zarf analizi ile incelenmiştir. Zarf analizi sonucu elde edilen veriler istatistiksel olarak incelenmiştir. İstatistiksel analizde ortalama, medyan, standart sapma, varyans, basıklık, çarpıklık, olasılık yoğunluk fonksiyonu ve kümülatif dağılım fonksiyonları hesaplanmıştır. Elde edilen sonuçlar istatistiksel analiz yöntemlerinin sincap-kafesli asenkron motorlarda rotor arızalarının incelenmesinde ve tespit edilmesinde kullanılabileceğini göstermektedir.

Evaluation of Rotor Faults of Induction Motors by Statistical Analysis Method

In this article, commonly occurring rotor faults of squirrel-cage induction motors will be statistically analyzed and evaluated. Rotor faults generally occur due to broken rotor bars and cracking of end-rings. Rotor faults are reflected into the stator current as harmonics. Various signal processing methods are used to detect these harmonics. These harmonics and other feature extraction methods are used to detect the faults of induction motor. In this work, the stator current is firstly studied by using envelope analysis. The data gained as result of Envelope analysis are studied statistically. In the statistical analysis mean, median, standard deviation, variance, kurtosis, skewness, probability density function and cumulative density function were calculated. The gained results indicate that the statistical analysis methods can be used for the detection of rotor faults of squirrel-cage induction motors.

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  • 1. Unsal A., Kabul A., "Detection of the broken rotor bars of squirrel-cage induction motors based on normalized least mean square filter and Hilbert envelope analysis", Electrical Engineering, 98: 245-256, (2016).
  • 2. Ünsal A., Kara Ö., "Detection and Classification of the Broken Rotor Bars in Squirrel-Cage Induction Motors", International Journal of Engineering Research And Management, 03: 59-64, (2016).
  • 3. Ünsal A, Karakaya O., "Asenkron Motor Rotor Arızalarının Analizi", Dumlupınar Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 34: 69-86, (2015).
  • 4. Ünsal A, Güçlü S, "Asenkron Motorlarda Rotor Çubuğu Kırıklarının Mann-Whitney U-Testi İle İncelenmesi", Dumlupınar Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 35: 79-92, (2015).
  • 5. Lamb M. J., "Monitoring the Structural Integrity of Packaging Materials Subjected to Sustained Random Loads", PhD Thesis, Victoria University, School of Engineering and Science, (2011).
  • 6. Günal S., Ece D. G., Gerek Ö. N., "Induction machine condition monitoring using notch-filtered motor current", Mechanical Systems and Signal Processing, 23: 2658- 2670, (2009).
  • 7. Thomson W.T., Gilmore R. J., "Motor current signature analysis to detect faults in induction motor drives - Fundamentals, data interpretation, and industrial case histories", Proceedings of the 32nd Turbomachinary Symposium, Texas, 145-156, (2003).
  • 8. Kalaskar C.S. and Gond V. J., "Motor current signature analysis to detect the fault in induction motor", International Journal of Engineering Research and Applications, 4: 58-61, (2014).
  • 9. Ayhan B., Chow M. Y., Trussell H. J., Song M. H., et. al., "Statistical analysis on a case study of load effect on PSD technique for induction motor broken rotor bar fault detection", Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Atlanta, 119- 123, (2003).
  • 10. Ahamed S. K., Karmakar S., Mitra M., Sengupta S., "Diagnosis of induction motor faults due to broken rotor bar and rotor mass unbalance through discrete wavelet transform of starting current at no load", Journal of Electrical Systems, 6(3): 442-456, (2010).
  • 11. Vargas M. H., Yepez E. C., Perez A. G. and Troncoso R. J. R., "Novel methodology for broken-rotor-bar and bearing faults detection through SVD and information entropy", Journal of Scientific&Industrial Research, 71: 589-593, (2012).
  • 12. Khwaja H.A., Gupta S. P. and Kumar V., "A statistical approach for fault diagnosis in electrical machines", IETE Journal of Research, 56 (3): 146-155, (2014).
  • 13. Perez O.D., Escudero L. A. G., Sotelo d. M., Gardel P. E. and Alonso M. P., "Analysis of fault signatures for the diagnosis of induction motors fed by voltage source inverters using ANOVA and additive models", Electric Power System Research 121: 1-13, (2015).
  • 14. İnternet: Kschischang F. R., "The hilbert transform", Lecture Notes, The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, (2006). http://www.comm.utoronto.ca/frank/papers/hilbert.pdf
  • 15. Poularikas A. D., "Hilbert Transform", Transforms and Applications Handbook, CRC Press , New York (2010).
  • 16. Aczel A. D., "The Bell-Shaped Curve", Statistics Concepts and Applications, IRWIN, United States of America, (1995).
  • 17. Internet: Freiwald R., "The cumulative distribution function for a random variable X", Lecture Notes, Washington University. http://www.math.wustl.edu/~freiwald/Math132/cdf.pdf