BASIC ALGORITHM FOR INDUCTION MOTORS ROTOR FAULTS PRE-DETERMINATION
DOI:
https://doi.org/10.17770/etr2017vol3.2526Keywords:
Electric machines, modeling, equivalent circuits, fault diagnosisAbstract
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
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.
