Research Article
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Year 2019, Volume: 3 Issue: 3, 156 - 163, 15.12.2019

Özgür Yılmaz Murat Aksoy Zehan Kesilmiş

https://doi.org/10.35860/iarej.451528
Cited By: 4

Abstract

References

  • 1. Bayrak, M., & Küçüker, A.,Üç fazli asenkron motorlardaki kirik rotor çubuǧu arizalarinin tespiti için güç tabanli bir algoritmanin geliştirilmesi. Journal of the Faculty of Engineering and Architecture of Gazi University, 2014.29(3),303–311. https://doi.org/10.17341/gummfd.82945
  • 2. Juan Carbajal-Hernández, J., Sánchez-Fernández, L. P., Hernández-Bautista, I., Medel-Juárez, J. de J., & Sánchez-Pérez, L. A. (2016). Classification of unbalance and misalignment in induction motors using orbital analysis and associative memories. Neurocomputing, 2016.175: p.838–850. https://doi.org/10.1016/j.neucom.2015.06.094
  • 3. Karahan, M. F., Titreşim analiziyle makinalarda arıza teşhisi. Yüksek Lisans Tezi, Celal Bayar Üniversitesi Fen Bilimleri Enstitüsü, Manisa,2005:p.37-55.
  • 4. Cerit, M., Makine Mühendisliği El Kitabı Cilt 1:Üretim veTasarım,TMMOB,1994(169):p.2-32.
  • 5. Kalyoncu, M., Titreşim analizi ile makina elemanları arızalarının belirlenmesi. Mühendis ve Makina Dergisi, Ankara,2006.47(552):p.28-35.
  • 6. Ayaz, E. Elektrik motorlarında dalgacık analizi yaklaşımı ile rulman arıza tanısı ve yapay zeka tabanlı bir durum izleme sistemi (Doctoral dissertation, Fen Bilimleri Enstitüsü),2001:p.55-62.
  • 7. Chen, J., Li, Z., Pan, J., Chen, G., Zi, Y., Yuan, J., He, Z., Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 2016.70:p.1-35. https://doi.org/10.1016/j.ymssp.2015.08.023
  • 8. Abbak, A., Jeodezide Zaman Dizilerinin Dalgacık (Wavelet) Analizi. Doktora Tezi,Selçuk Üniversitesi,Fen Bilimleri Enstitüsü,Konya,2007:p.8-17.
  • 9. Chandra, N. H., & Sekhar, A. S., Fault detection in rotor bearing systems using time frequency techniques. Mechanical Systems and Signal Processing, 2016.72(73):p.105–133. https://doi.org/10.1016/j.ymssp.2015.11.013
  • 10. Patil, SS, Gaikwad, JA,Vibration analysis of electrical rotating machines using FFT: a method of predictive maintenance. In: 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), Tiruchengode, India, 4–6 July 2013, pp.1–6.
  • 11. Lu, J., Ming, T., & Zhang, C. (2019). Simulation Research of Rotor Misalignment Fault Based on Adams. IOP Conference Series: Earth and Environmental Science, 2019.252(2):p.022148. https://doi.org/10.1088/1755-1315/252/2/022148
  • 12. Raj, V. P., Natarajan, K., & Girikumar, T. G. (2013, October). Induction motor fault detection and diagnosis by vibration analysis using MEMS accelerometer. In Emerging Trends in Communication, Control, Signal Processing & Computing Applications (C2SPCA), IEEE International Conference,2013(2013):p. 1-6.
  • 13. Thomson, W., Theory of vibration with applications. CrC Press,2018:p.17-24.
  • 14. Köse, R. K., Makine arızalarının belirlenmesinde titreşim analizi. Mühendis ve Makine Dergisi, 2004(45).538:p:24-32.
  • 15. Cveticanin, L., Vecseri, A., Bíró, I., & Cveticanin, D, Detection Procedures For Shaft Misalignment detection:An Overview. Annals of the Faculty of Engineering Hunedoara-International Journal of Engineering,2019.17(1):p.1-4.
  • 16. Ding, H., & Sun, Y.,Rolling bearing fault feature extraction based on Daubechies wavelet decomposition. IEEE 37th Chinese Control Conference, 2018:pp.8645-8649.
  • 17. Song, W., Xiang, J., & Zhong, Y. A simulation model based fault diagnosis method for bearings. Journal of Intelligent & Fuzzy Systems, 2018.34(6): p.3857-3867.
  • 18. Tezcan, M.,Gökhan, A., Canakoglu, A., Turan, M., Üç Fazli Asenkron Motor Tasarimi ve FFT Analizi Three Phase Induction Motor Design and FFT Analysis, [cited 2019 09 July];Available from: http://www.emo.org.tr/ekler/3f79341d72939e6_ek.pdf
  • 19. Wang, J., Zhao, B., & Zhou, H., Rolling bear fault recognition based on improved sparse decomposition. IEEE 37th Chinese Control Conference 2018:p. 5790-5794.
  • 20. Hines, A. J. W., Jesse, S., Edmondson, A., & Nower, D., Motor Shaft Misalignment versus Bearing Load Analysis: Study Shows Shaft Misalignment Reduces Bearing Life.Maintenance Technology,1999(April):p.11–77.
  • 21. Kumar, C., Krishnan, G., & Sarangi, S., Experimental investigation on misalignment fault detection in induction motors using current and vibration signature analysis. In Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), IEEE International Conference,2015:p. 61-66.

Misalignment fault detection by wavelet analysis of vibration signals

Year 2019, Volume: 3 Issue: 3, 156 - 163, 15.12.2019

Özgür Yılmaz Murat Aksoy Zehan Kesilmiş

https://doi.org/10.35860/iarej.451528
Cited By: 4

Abstract

Asynchronous motors are
frequently used in many industrial applications, especially pumps and fans. Placement,
bearing and coupling faults are common faults in these types of engines. Misalignment
error is a common type of error that is seen very often among these errors. This
error may cause efficiency decrease in a short run and vibration may cause
short circuit and wear in moving parts in the stator windings in a long run. Early
diagnosis of such faults is important in terms of machine health and
productivity. In this study, loose connection and angular imbalance of the
asynchronous machine were investigated. In the experimental works, a 1 Phase 0.75
KW power asynchronous motor, Y-0036-024A Electromagnetic Brake and SKF Microlog
vibration meter were used during the measurements. The Frequency components of
motor caused by the settlement errors were investigated under the different
loads. A loose assembly error and angular imbalance were investigated from the
misalignment errors. The engine was run idle and without any positioning errors
and measurements were taken from different points with the accelerometer and
the frequency spectrum examined. Measurements are repeated when the
misalignment errors are occurred on purpose and the FFT frequency components
were compared under the load of 12.50Nm using magnetic brake. The results show
that the FFT frequency components are examined and the placement error can be
determined with high success and accuracy. It has been found that harmonic components
are formed in the frequency spectrum at 25Hz Coefficients. After the settlement
error is generated it is seen that, undesired frequency components that are
unloaded are lowered under load when the frequency spectra is examined. In this
study, theoretical and experimental comparisons of settlement errors are made.
Although many errors in this subject are examined in the same publication in
general, only the results of the settlement errors are examined specifically as
a contribution to the literature. The results and graphs are presented
comparatively to the reader's knowledge.

Keywords

Asynchronous motors Misalignment fault Vibration frequency spectrum

References

  • 1. Bayrak, M., & Küçüker, A.,Üç fazli asenkron motorlardaki kirik rotor çubuǧu arizalarinin tespiti için güç tabanli bir algoritmanin geliştirilmesi. Journal of the Faculty of Engineering and Architecture of Gazi University, 2014.29(3),303–311. https://doi.org/10.17341/gummfd.82945
  • 2. Juan Carbajal-Hernández, J., Sánchez-Fernández, L. P., Hernández-Bautista, I., Medel-Juárez, J. de J., & Sánchez-Pérez, L. A. (2016). Classification of unbalance and misalignment in induction motors using orbital analysis and associative memories. Neurocomputing, 2016.175: p.838–850. https://doi.org/10.1016/j.neucom.2015.06.094
  • 3. Karahan, M. F., Titreşim analiziyle makinalarda arıza teşhisi. Yüksek Lisans Tezi, Celal Bayar Üniversitesi Fen Bilimleri Enstitüsü, Manisa,2005:p.37-55.
  • 4. Cerit, M., Makine Mühendisliği El Kitabı Cilt 1:Üretim veTasarım,TMMOB,1994(169):p.2-32.
  • 5. Kalyoncu, M., Titreşim analizi ile makina elemanları arızalarının belirlenmesi. Mühendis ve Makina Dergisi, Ankara,2006.47(552):p.28-35.
  • 6. Ayaz, E. Elektrik motorlarında dalgacık analizi yaklaşımı ile rulman arıza tanısı ve yapay zeka tabanlı bir durum izleme sistemi (Doctoral dissertation, Fen Bilimleri Enstitüsü),2001:p.55-62.
  • 7. Chen, J., Li, Z., Pan, J., Chen, G., Zi, Y., Yuan, J., He, Z., Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 2016.70:p.1-35. https://doi.org/10.1016/j.ymssp.2015.08.023
  • 8. Abbak, A., Jeodezide Zaman Dizilerinin Dalgacık (Wavelet) Analizi. Doktora Tezi,Selçuk Üniversitesi,Fen Bilimleri Enstitüsü,Konya,2007:p.8-17.
  • 9. Chandra, N. H., & Sekhar, A. S., Fault detection in rotor bearing systems using time frequency techniques. Mechanical Systems and Signal Processing, 2016.72(73):p.105–133. https://doi.org/10.1016/j.ymssp.2015.11.013
  • 10. Patil, SS, Gaikwad, JA,Vibration analysis of electrical rotating machines using FFT: a method of predictive maintenance. In: 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), Tiruchengode, India, 4–6 July 2013, pp.1–6.
  • 11. Lu, J., Ming, T., & Zhang, C. (2019). Simulation Research of Rotor Misalignment Fault Based on Adams. IOP Conference Series: Earth and Environmental Science, 2019.252(2):p.022148. https://doi.org/10.1088/1755-1315/252/2/022148
  • 12. Raj, V. P., Natarajan, K., & Girikumar, T. G. (2013, October). Induction motor fault detection and diagnosis by vibration analysis using MEMS accelerometer. In Emerging Trends in Communication, Control, Signal Processing & Computing Applications (C2SPCA), IEEE International Conference,2013(2013):p. 1-6.
  • 13. Thomson, W., Theory of vibration with applications. CrC Press,2018:p.17-24.
  • 14. Köse, R. K., Makine arızalarının belirlenmesinde titreşim analizi. Mühendis ve Makine Dergisi, 2004(45).538:p:24-32.
  • 15. Cveticanin, L., Vecseri, A., Bíró, I., & Cveticanin, D, Detection Procedures For Shaft Misalignment detection:An Overview. Annals of the Faculty of Engineering Hunedoara-International Journal of Engineering,2019.17(1):p.1-4.
  • 16. Ding, H., & Sun, Y.,Rolling bearing fault feature extraction based on Daubechies wavelet decomposition. IEEE 37th Chinese Control Conference, 2018:pp.8645-8649.
  • 17. Song, W., Xiang, J., & Zhong, Y. A simulation model based fault diagnosis method for bearings. Journal of Intelligent & Fuzzy Systems, 2018.34(6): p.3857-3867.
  • 18. Tezcan, M.,Gökhan, A., Canakoglu, A., Turan, M., Üç Fazli Asenkron Motor Tasarimi ve FFT Analizi Three Phase Induction Motor Design and FFT Analysis, [cited 2019 09 July];Available from: http://www.emo.org.tr/ekler/3f79341d72939e6_ek.pdf
  • 19. Wang, J., Zhao, B., & Zhou, H., Rolling bear fault recognition based on improved sparse decomposition. IEEE 37th Chinese Control Conference 2018:p. 5790-5794.
  • 20. Hines, A. J. W., Jesse, S., Edmondson, A., & Nower, D., Motor Shaft Misalignment versus Bearing Load Analysis: Study Shows Shaft Misalignment Reduces Bearing Life.Maintenance Technology,1999(April):p.11–77.
  • 21. Kumar, C., Krishnan, G., & Sarangi, S., Experimental investigation on misalignment fault detection in induction motors using current and vibration signature analysis. In Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), IEEE International Conference,2015:p. 61-66.
There are 21 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Özgür Yılmaz 0000-0003-0972-0226

Murat Aksoy 0000-0002-6980-5902

Zehan Kesilmiş This is me 0000-0002-5781-9450

Publication Date December 15, 2019
Submission Date August 7, 2018
Acceptance Date October 10, 2019
Published in Issue Year 2019 Volume: 3 Issue: 3

Cite

APA Yılmaz, Ö., Aksoy, M., & Kesilmiş, Z. (2019). Misalignment fault detection by wavelet analysis of vibration signals. International Advanced Researches and Engineering Journal, 3(3), 156-163. https://doi.org/10.35860/iarej.451528
AMA Yılmaz Ö, Aksoy M, Kesilmiş Z. Misalignment fault detection by wavelet analysis of vibration signals. Int. Adv. Res. Eng. J. December 2019;3(3):156-163. doi:10.35860/iarej.451528
Chicago Yılmaz, Özgür, Murat Aksoy, and Zehan Kesilmiş. “Misalignment Fault Detection by Wavelet Analysis of Vibration Signals”. International Advanced Researches and Engineering Journal 3, no. 3 (December 2019): 156-63. https://doi.org/10.35860/iarej.451528.
EndNote Yılmaz Ö, Aksoy M, Kesilmiş Z (December 1, 2019) Misalignment fault detection by wavelet analysis of vibration signals. International Advanced Researches and Engineering Journal 3 3 156–163.
IEEE Ö. Yılmaz, M. Aksoy, and Z. Kesilmiş, “Misalignment fault detection by wavelet analysis of vibration signals”, Int. Adv. Res. Eng. J., vol. 3, no. 3, pp. 156–163, 2019, doi: 10.35860/iarej.451528.
ISNAD Yılmaz, Özgür et al. “Misalignment Fault Detection by Wavelet Analysis of Vibration Signals”. International Advanced Researches and Engineering Journal 3/3 (December 2019), 156-163. https://doi.org/10.35860/iarej.451528.
JAMA Yılmaz Ö, Aksoy M, Kesilmiş Z. Misalignment fault detection by wavelet analysis of vibration signals. Int. Adv. Res. Eng. J. 2019;3:156–163.
MLA Yılmaz, Özgür et al. “Misalignment Fault Detection by Wavelet Analysis of Vibration Signals”. International Advanced Researches and Engineering Journal, vol. 3, no. 3, 2019, pp. 156-63, doi:10.35860/iarej.451528.
Vancouver Yılmaz Ö, Aksoy M, Kesilmiş Z. Misalignment fault detection by wavelet analysis of vibration signals. Int. Adv. Res. Eng. J. 2019;3(3):156-63.

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