International Journal of Prognostics and Health Management, ISSN 2153-2648, 2015 007 Effectiveness of vibration monitoring in the health assessment of induction motor
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Published
Nov 3, 2020
T Ch Anil Kumar
Gurmeet Singh
V N A Naikan
Abstract
Induction motors are widely used prime movers for rotating machinery in most of the industrial applications. Reliability of the induction motors plays a significant role in reduction in downtime of the machinery. Proper diagnostic procedures are to be followed to assess the condition of the motor. Based on the literature, it is found that the vibration analysis is mostly used for diagnosis of rotating systems. However, industrial experiences reveal that potential motor failures have been observed very frequently even with proper vibration based condition monitoring. Therefore, this paper focuses on further investigations of whether the vibration monitoring stand alone can properly diagnose the presence of faults such as eccentricity in an induction motor or any other alternative technique should be employed in addition to the vibration monitoring for better diagnosis. In this paper, experimental investigations have been carried out to monitor the changes in the vibration and current spectrum of an eccentric motor in its decoupled and coupled state with a healthy rotor system.
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Keywords
condition monitoring, vibration analysis, Eccentricity, MCSA, Hierarchical clustering
References
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Mehrjou, M. R., Mariun, N., Hamiruce Marhaban, M., & Misron, N. (2011). Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review. Mechanical Systems and Signal Processing, 25(8), 2827-2848.
Sinha, J. K., & Elbhbah, K. (2013). A future possibility of vibration based condition monitoring of rotating machines. Mechanical Systems and Signal Processing, 34(1), 231-240.
Tandon, N. (1994). A comparison of some vibration parameters for the condition monitoring of rolling element bearings. Measurement, 12(3), 285-289.
Germen, E., Başaran, M., & Fidan, M. (2014). Sound based induction motor fault diagnosis using Kohonen self-organizing map. Mechanical Systems and Signal Processing, 46(1), 45-58.
Loutas, T. H., Roulias, D., Pauly, E., & Kostopoulos, V. (2011). The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mechanical systems and signal processing, 25(4), 1339-1352.
Acosta, G. G., Verucchi, C. J., & Gelso, E. R. (2006). A current monitoring system for diagnosing electrical failures in induction motors. Mechanical Systems and Signal Processing, 20(4), 953-965.
Didier, G., Ternisien, E., Caspary, O., & Razik, H. (2007). A new approach to detect broken rotor bars in induction machines by current spectrum analysis.Mechanical Systems and Signal Processing, 21(2), 1127-1142.
MA Cruz, AJ Marques Cardoso, S. (2000). Rotor cage fault diagnosis in three-phase induction motors by extended Park's vector approach. Electric Machines &Power Systems, 28(4), 289-299.
Bagavathiappan, S., Lahiri, B. B., Saravanan, T., Philip, J., & Jayakumar, T. (2013). Infrared thermography for condition monitoring–a review. Infrared Physics & Technology, 60, 35-55.
Thorsen, O. V., & Dalva, M. (1995). A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals, and oil refineries.Industry Applications, IEEE Transactions on, 31(5), 1186-1196.
Benbouzid, M. E. H., & Kliman, G. B. (2003). What stator current processing-based technique to use for induction motor rotor faults diagnosis?. Energy Conversion, IEEE Transactions on, 18(2), 238-244.
Verma, A. K., Sarangi, S., & Kolekar, M. H. (2014). Experimental Investigation of Misalignment Effects on Rotor Shaft Vibration and on Stator Current Signature. Journal of Failure Analysis and Prevention, 14(2), 125-138.
Barbour, A., & Thomson, W. T. (1997, September). Finite element analysis and on-line current monitoring to diagnose air gap eccentricity in 3-phase induction motors. In Electrical Machines and Drives, 1997 Eighth International Conference on (Conf. Publ. No. 444) (pp. 150-154). IET.
El Hachemi Benbouzid, M. (2000). A review of induction motors signature analysis as a medium for faults detection. Industrial Electronics, IEEE Transactions on, 47(5), 984-993.
Obaid, R. R., & Habetler, T. G. (2003, August). Effect of load on detecting mechanical faults in small induction motors. In Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003. 4th IEEE International Symposium on (pp. 307-311). IEEE.
Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors-a review. Energy Conversion, IEEE Transactions on, 20(4), 719-729.
Toliyat, H. A., Arefeen, M. S., & Parlos, A. G. (1996). A method for dynamic simulation of air-gap eccentricity in induction machines. Industry Applications, IEEE Transactions on, 32(4), 910-918.
Finley, W. R., Hodowanec, M. M., & Holter, W. G. (1999). An analytical approach to solving motor vibration problems. In Petroleum and Chemical Industry Conference, 1999. Industry Applications Society 46th Annual (pp. 217-232). IEEE.
Liang, B., Payne, B. S., Ball, A. D., & Iwnicki, S. D. (2002). Simulation and fault detection of three-phase induction motors. Mathematics and computers in simulation, 61(1), 1-15.
Holopainen, T. P., Tenhunen, A., & Arkkio, A. (2005). Electromechanical interaction in rotordynamics of cage induction motors. Journal of sound and vibration, 284(3), 733-755.
Arkan, M., Çaliş, H., & Tağluk, M. E. (2005). Bearing and misalignment fault detection in induction motors by using the space vector angular fluctuation signal. Electrical Engineering, 87(4), 197-206.
Bae, H., Kim, Y. T., Lee, S. H., Kim, S., & Lee, M. H. (2005). Fault diagnostic of induction motors for equipment reliability and health maintenance based upon Fourier and wavelet analysis. Artificial Life and Robotics, 9(3), 112-116.
Antonino-Daviu, J., Jover, P., Riera, M., Arkkio, A., & Roger-Folch, J. (2007). DWT analysis of numerical and experimental data for the diagnosis of dynamic eccentricities in induction motors. Mechanical Systems and Signal Processing,21(6), 2575-2589.
Ebrahimi, B. M., Etemadrezaei, M., & Faiz, J. (2011). Dynamic eccentricity fault diagnosis in round rotor synchronous motors. Energy Conversion and Management, 52(5), 2092-2097.
Maruthi, G. S., & Hegde, V. (2013, December). Department of Electrical and Electronics Engineering Smt. LV Government Polytechnic, Hassan 573201 Karnataka State, India. In Condition Assessment Techniques in Electrical Systems (CATCON), 2013 IEEE 1st International Conference on (pp. 207-212). IEEE.
Duda, R. O., Hart, P. E., & Stork, D. G. (1999). Pattern classification. John Wiley & Sons.
Mehrjou, M. R., Mariun, N., Hamiruce Marhaban, M., & Misron, N. (2011). Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review. Mechanical Systems and Signal Processing, 25(8), 2827-2848.
Sinha, J. K., & Elbhbah, K. (2013). A future possibility of vibration based condition monitoring of rotating machines. Mechanical Systems and Signal Processing, 34(1), 231-240.
Tandon, N. (1994). A comparison of some vibration parameters for the condition monitoring of rolling element bearings. Measurement, 12(3), 285-289.
Germen, E., Başaran, M., & Fidan, M. (2014). Sound based induction motor fault diagnosis using Kohonen self-organizing map. Mechanical Systems and Signal Processing, 46(1), 45-58.
Loutas, T. H., Roulias, D., Pauly, E., & Kostopoulos, V. (2011). The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mechanical systems and signal processing, 25(4), 1339-1352.
Acosta, G. G., Verucchi, C. J., & Gelso, E. R. (2006). A current monitoring system for diagnosing electrical failures in induction motors. Mechanical Systems and Signal Processing, 20(4), 953-965.
Didier, G., Ternisien, E., Caspary, O., & Razik, H. (2007). A new approach to detect broken rotor bars in induction machines by current spectrum analysis.Mechanical Systems and Signal Processing, 21(2), 1127-1142.
MA Cruz, AJ Marques Cardoso, S. (2000). Rotor cage fault diagnosis in three-phase induction motors by extended Park's vector approach. Electric Machines &Power Systems, 28(4), 289-299.
Bagavathiappan, S., Lahiri, B. B., Saravanan, T., Philip, J., & Jayakumar, T. (2013). Infrared thermography for condition monitoring–a review. Infrared Physics & Technology, 60, 35-55.
Thorsen, O. V., & Dalva, M. (1995). A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals, and oil refineries.Industry Applications, IEEE Transactions on, 31(5), 1186-1196.
Benbouzid, M. E. H., & Kliman, G. B. (2003). What stator current processing-based technique to use for induction motor rotor faults diagnosis?. Energy Conversion, IEEE Transactions on, 18(2), 238-244.
Verma, A. K., Sarangi, S., & Kolekar, M. H. (2014). Experimental Investigation of Misalignment Effects on Rotor Shaft Vibration and on Stator Current Signature. Journal of Failure Analysis and Prevention, 14(2), 125-138.
Barbour, A., & Thomson, W. T. (1997, September). Finite element analysis and on-line current monitoring to diagnose air gap eccentricity in 3-phase induction motors. In Electrical Machines and Drives, 1997 Eighth International Conference on (Conf. Publ. No. 444) (pp. 150-154). IET.
El Hachemi Benbouzid, M. (2000). A review of induction motors signature analysis as a medium for faults detection. Industrial Electronics, IEEE Transactions on, 47(5), 984-993.
Obaid, R. R., & Habetler, T. G. (2003, August). Effect of load on detecting mechanical faults in small induction motors. In Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003. 4th IEEE International Symposium on (pp. 307-311). IEEE.
Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors-a review. Energy Conversion, IEEE Transactions on, 20(4), 719-729.
Toliyat, H. A., Arefeen, M. S., & Parlos, A. G. (1996). A method for dynamic simulation of air-gap eccentricity in induction machines. Industry Applications, IEEE Transactions on, 32(4), 910-918.
Finley, W. R., Hodowanec, M. M., & Holter, W. G. (1999). An analytical approach to solving motor vibration problems. In Petroleum and Chemical Industry Conference, 1999. Industry Applications Society 46th Annual (pp. 217-232). IEEE.
Liang, B., Payne, B. S., Ball, A. D., & Iwnicki, S. D. (2002). Simulation and fault detection of three-phase induction motors. Mathematics and computers in simulation, 61(1), 1-15.
Holopainen, T. P., Tenhunen, A., & Arkkio, A. (2005). Electromechanical interaction in rotordynamics of cage induction motors. Journal of sound and vibration, 284(3), 733-755.
Arkan, M., Çaliş, H., & Tağluk, M. E. (2005). Bearing and misalignment fault detection in induction motors by using the space vector angular fluctuation signal. Electrical Engineering, 87(4), 197-206.
Bae, H., Kim, Y. T., Lee, S. H., Kim, S., & Lee, M. H. (2005). Fault diagnostic of induction motors for equipment reliability and health maintenance based upon Fourier and wavelet analysis. Artificial Life and Robotics, 9(3), 112-116.
Antonino-Daviu, J., Jover, P., Riera, M., Arkkio, A., & Roger-Folch, J. (2007). DWT analysis of numerical and experimental data for the diagnosis of dynamic eccentricities in induction motors. Mechanical Systems and Signal Processing,21(6), 2575-2589.
Ebrahimi, B. M., Etemadrezaei, M., & Faiz, J. (2011). Dynamic eccentricity fault diagnosis in round rotor synchronous motors. Energy Conversion and Management, 52(5), 2092-2097.
Maruthi, G. S., & Hegde, V. (2013, December). Department of Electrical and Electronics Engineering Smt. LV Government Polytechnic, Hassan 573201 Karnataka State, India. In Condition Assessment Techniques in Electrical Systems (CATCON), 2013 IEEE 1st International Conference on (pp. 207-212). IEEE.
Duda, R. O., Hart, P. E., & Stork, D. G. (1999). Pattern classification. John Wiley & Sons.
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Technical Papers