BASIC ALGORITHM FOR INDUCTION MOTORS ROTOR FAULTS PRE-DETERMINATION

Authors

  • Aleksei Petrov Department of Electrical Drives and Automation Systems, Pskov State University, Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology
  • Anton Rassolkin Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology
  • Toomas Vaimann Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology
  • Anouar Belahcen Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Department of Electrical Engineering and Automation, Aalto University, FINLAND
  • Ants Kallaste Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology
  • Igor Plokhov Department of Electrical Drives and Automation Systems, Pskov State University

DOI:

https://doi.org/10.17770/etr2017vol3.2526

Keywords:

Electric machines, modeling, equivalent circuits, fault diagnosis

Abstract

Due to importance of squirrel cage induction motor in today’s industry, the fault detection on that type of motors has become a highly developed area of interest for researchers. The electrical machine is designed for stable operations with minimum noise and vibrations under the normal conditions. When the fault emerges, some additional distortions appear. The necessity to detect the fault in an early stage, to prevent further damage of the equipment due to fault propagation, is one of the most important features of any condition monitoring or diagnostic techniques for electrical machines nowadays. In this paper possible induction motors faults classified and basic algorithm for rotor faults pre-determination is presented.

Downloads

Download data is not yet available.

Author Biographies

  • Aleksei Petrov, Department of Electrical Drives and Automation Systems, Pskov State University, Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology
    Aleksei Petrov was born in Pskov, Russian Federation in 1991 and received his engineer's degree from Pskov State University in 2013. He is currently a PhD student of the department Electric Drive and Automation Systems at Pskov State University. His research interests lie in the field of mathematical modeling and electrical machines.Aleksei Vasil'evich Petrov was born in Pskov, Russian Federation in 1991 and received his engineer's degree from Pskov State University in 2013. He is currently a PhD student of the department Electric Drive and Automation Systems at Pskov State University. His research interests lie in the field of mathematical modeling and electrical machines.
  • Anton Rassolkin, Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology
    Anton Rassõlkin was born in Tallinn, Estonia, in 1985 and received the BSc, MSc and PhD degrees in electric drives and power electronics from Tallinn University of Technology, Tallinn, Estonia, in 2008, 2010 and 2014, respectively. In 2010 received Dipl.-Ing. (FH) degree in automatic from University of Applied Science Giessen-Friedberg, Giessen, Germany. He has been working in several companies as an electrical engineer. Presently he is working at the Tallinn University of Technology, Department of Electrical Engineering as a research scientist. He is the member of IEEE (S’12-M’16). The main research interests lay in the field of electric drives and its control systems as well as in the fields of electrical machines and electric transportation.
  • Toomas Vaimann, Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology
    Toomas Vaimann was born in Pärnu, Estonia, in 1984 and received his BSc, MSc and PhD degrees in electrical engineering from Tallinn University of Technology, Estonia, in 2007, 2009 and 2014 respectively. He is currently a senior researcher in Tallinn University of Technology, Department of Electrical Engineering and carrying out postdoctoral research at the Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland. He has been working in several companies as an electrical engineer. He is the member of IEEE (S’11-M’14), Estonian Society of Moritz Hermann Jacobi and Estonian Society for Electrical Power Engineering. His main research interest is the diagnostics of electrical machines.
  • Anouar Belahcen, Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Department of Electrical Engineering and Automation, Aalto University, FINLAND
    Anouar Belahcen was born in Essaouira, Morocco, in 1963. He received the BSc degree in physics from the University Sidi Mohamed Ben Abdellah, Fes, Morocco, in 1988 and the MSc (Tech.) and Doctor (Tech.) degrees from Helsinki University of Technology, Finland, in 1998, and 2004, respectively. From 2008 to 2013, he has been working as Adjunct Professor in the field of coupled problems and material modeling at Aalto University, Finland. Since 2011 he is Professor of electrical machines at Tallinn University of Technology, Estonia and in 2013 he became Professor of Energy and Power at Aalto University. His research interest are numerical modeling of electrical machines, especially magnetic material modeling, coupled magnetic and mechanical problems, magnetic forces, and magnetostriction.
  • Ants Kallaste, Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology
    Ants Kallaste was born in Pärnu, Estonia in 1980 and received his BSc, MSc and PhD degrees in electrical engineering from Tallinn University of Technology, Estonia, in 2004, 2006 and 2013 respectively. He is currently a senior researcher in Tallinn University of Technology, Department of Electrical Engineering and carrying out postdoctoral research at the Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland. He has been working in several companies as an electrical engineer. Presently he is working at the Tallinn University of Technology, Department of Electrical Engineering holding the position of Head of Chair of Electrical Machines. He has been the IEEE member since 2013. His main research interests include permanent magnet machine design and wind turbines.
  • Igor Plokhov, Department of Electrical Drives and Automation Systems, Pskov State University
    Igor Plokhov was born in Pskov, Russian Federation in 1959 and received his engineer's, Candidate of Sciences and DSc degree from Saint-Petersburg Polytechnic University 1983, 1992 and 2014 respectively. He was working as assistant, lecturer, associate professor, professor and vice-rector for science in Pskov State University. He is currently a head of Department of Electrical Drives and Automation Systems and head of the research activities in Pskov State University.He is an editor of the journal "Bulletin of the Pskov State University," a series of "Technical Science", a member of the dissertation council D212.229.11 at St. Petersburg State University. He is a corresponding member of the International Higher Education Academy of Sciences.The main research interests lay in the field of electric drives, electrical machines and autonomous energy systems.

References

M. A. A. Elmaleeh, N. Saad, N. Ahmed, and M. Awan, “On-line fault detection & diagnosis of rotating machines using acoustic emission monitoring techniques,” in 2007 International Conference on Intelligent and Advanced Systems, 2007, pp. 897–900.

P. S. Bhowmik, P. S. Bhowmik, S. Pradhan, and M. Prakash, “Fault Diagnostic and Monitoring Methods of Induction Motor: A Review.”

L. G. Sidel’nikov and D. O. Afanas’ev, “Control Methods Review of Induction Motors Technical State During Operation”, Perm National Research Polytechnic University Gazette,” Jpurnal Perm Natl. Res. Polytech. Univ. (In Russ., no. 7, pp. 127–137, 2013.

M. R. Mehrjou, N. Mariun, M. Hamiruce Marhaban, and N. Misron, “Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review,” Mech. Syst. Signal Process., vol. 25, no. 8, pp. 2827–2848, Nov. 2011.

S. Nandi, H. A. Toliyat, and X. Li, “Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review,” IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 719–729, Dec. 2005.

J. Sottile, F. C. Trutt, and J. L. Kohler, “Experimental investigation of on-line methods for incipient fault detection [in induction motors],” in Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129), 2000, vol. 4, pp. 2682–2687.

A. Kallaste, A. Belahcen, A. Kilk, and T. Vaimann, “Analysis of the eccentricity in a low-speed slotless permanent-magnet wind generator,” in 2012 Electric Power Quality and Supply Reliability, 2012, pp. 1–6.

W. Li, “Detection of Induction Motor Faults: A Comparison of Stator Current, Vibration and Acoustic Methods,” J. Vib. Control, vol. 12, no. 2, pp. 165–188, Feb. 2006.

D. S. Shah and V. N. Patel, “A Review of Dynamic Modeling and Fault Identifications Methods for Rolling Element Bearing,” Procedia Technol., vol. 14, pp. 447–456, 2014.

N. Tandon and A. Choudhury, “A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings,” Tribol. Int., vol. 32, no. 8, pp. 469–480, Aug. 1999.

B. Torcianti, C. Cristalli, and J. Vass, “Non-Contact Measurement for Mechanical Fault detection in Production Line,” in 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2007, pp. 297–301.

J. Sobra, T. Vaimann, and A. Belahcen, “Mechanical vibration analysis of induction machine under dynamic rotor eccentricity,” in 2016 17th International Scientific Conference on Electric Power Engineering (EPE), 2016, pp. 1–4.

A. Gaylard, “Acoustic evaluation of faults in electrical machines,” in Seventh International Conference on Electrical Machines and Drives, 1995, vol. 1995, pp. 147–150.

S. P. Verma and W. Li, “Measurement of Vibrations and Radiated Acoustic Noise of Electrical Machines,” Electrical Machines and Systems, 2003. ICEMS 2003. Sixth International Conference on, vol. 2. pp. 861–866 vol.2, 2003.

Xiaoqin Ma, Weisheng Lu, Xiangtian Chun, and Hengkun Xie, “Acoustical technology applications in large high voltage motors,” in Proceedings of 2001 International Symposium on Electrical Insulating Materials (ISEIM 2001). 2001 Asian Conference on Electrical Insulating Diagnosis (ACEID 2001). 33rd Symposium on Electrical and Electronic Insulating Materials and Applications in System, 2001, pp. 737–740.

A. J. Ellison and S. J. Yang, “Effects of rotor eccentricity on acoustic noise from induction machines,” Proc. Inst. Electr. Eng., vol. 118, no. 1, p. 174, 1971.

M. Janda, O. Vitek, and M. Skalka, “Noise diagnostic of induction machine,” in The XIX International Conference on Electrical Machines - ICEM 2010, 2010, pp. 1–4.

S. P. Verma, “Noise and vibrations of electrical machines and drives; their production and means of reduction,” in Proceedings of International Conference on Power Electronics, Drives and Energy Systems for Industrial Growth, 1996, vol. 2, pp. 1031–1037.

W. Doorsamy and W. A. Cronje, “A study on Bayesian spectrum estimation based diagnostics in electrical rotating machines,” in 2014 IEEE International Conference on Industrial Technology (ICIT), 2014, pp. 636–640.

A. Rassolkin et al., “Adjusted electrical equivalent circuit model of induction motor with broken rotor bars,” in 2016 Electric Power Quality and Supply Reliability (PQ), 2016, pp. 213–218.

D. May and P. Ossenberg, “Fit for science a course for teaching to organize, perform and present scientific work in engineering with mobile devices,” in 2015 IEEE Global Engineering Education Conference (EDUCON), 2015, pp. 176–183.

M. A. Bochicchio, M. Zappatore, and A. Longo, “Using Mobile Crowd Sensing to teach technology and entrepreneurship in high schools: An experience from Southern Italy,” in 2015 IEEE Global Engineering Education Conference (EDUCON), 2015, pp. 948–953.

J. Lim, S. J. Lee, G. Tewolde, and J. Kwon, “Ultrasonic-sensor deployment strategies and use of smartphone sensors for mobile robot navigation in indoor environment,” in IEEE International Conference on Electro/Information Technology, 2014, pp. 593–598.

A. Anjum and M. U. Ilyas, “Activity recognition using smartphone sensors,” in 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC), 2013, pp. 914–919.

X. Xu et al., “Advances in Smartphone-Based Point-of-Care Diagnostics,” Proc. IEEE, vol. 103, no. 2, pp. 236–247, Feb. 2015.

J. Lee, J. Jung, J. Lee, and Y. T. Kim, “Acute myocardial infarction detection system using ECG signal and cardiac marker detection,” in IEEE SENSORS 2014 Proceedings, 2014, pp. 2255–2257.

N. K. Verma, S. Sarkar, S. Dixit, R. K. Sevakula, and A. Salour, “Android app for intelligent CBM,” in 2013 IEEE International Symposium on Industrial Electronics, 2013, pp. 1–6.

W. Li, “Detection of Induction Motor Faults: A Comparison of Stator Current, Vibration and Acoustic Methods,” J. Vib. Control, vol. 12, no. 2, pp. 165–188, Feb. 2006.

M. A. A. Elmaleeh, N. Saad, and M. Awan, “Condition monitoring of industrial process plant using acoustic emission techniques,” in 2010 International Conference on Intelligent and Advanced Systems, 2010, pp. 1–6.

P. Rzeszucinski, M. Orman, C. T. Pinto, A. Tkaczyk, and M. Sulowicz, “A signal processing approach to bearing fault detection with the use of a mobile phone,” in 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2015, pp. 310–315.

T. Vaimann, A. Belahcen, and A. Kallaste, “Necessity for implementation of inverse problem theory in electric machine fault diagnosis,” in 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2015, pp. 380–385.

Downloads

Published

2017-06-15

How to Cite

[1]
A. Petrov, A. Rassolkin, T. Vaimann, A. Belahcen, A. Kallaste, and I. Plokhov, “BASIC ALGORITHM FOR INDUCTION MOTORS ROTOR FAULTS PRE-DETERMINATION”, ETR, vol. 3, pp. 254–257, Jun. 2017, doi: 10.17770/etr2017vol3.2526.