Parameter Estimation Using Particle Filter for Induction Machines under Inter-Turn Fault
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Published
Jul 14, 2017
Viethung Nguyen
Danwei Wang
Jeevanand Seshadrinath
Abhisek Ukil
Viswanathan Vaiyapuri
Sivakumar Nadarajan
Abstract
Parameter estimation has found its applications in various domains. In this paper, it is applied for fault severity estimation. A method, using particle filter approach, for estimating unknown constant fault parameters in stator winding inter-turn short is proposed. These parameters are insulation resistance and percentage of shorted turns. The method uses only measurements of stator voltages and currents. In order to effectively estimate the parameters, firstly, a sequence components-based approach is applied to derive an equality constraint on the magnitude of a state variable, which works as additional information for estimation algorithm based on state-state model. Secondly, the variance reduction technique is applied to increase the accuracy of the method.
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Keywords
PHM
References
Henao, H., Capolino, G.A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., Pusca, R., Estima, J., Riera-Guasp, M. and Hedayati-Kia, S (2014). Trends in fault diagnosis for electrical machines: A review of diagnostic techniques. IEEE industrial electronics magazine, vol. ~8, no. 2, pp. 31-42.
Arkan, M., Perovic, D. K., & Unsworth, P. (2001). Online stator fault diagnosis in induction motors. IEE Proceedings-Electric Power Applications, vol. 148, no. 6, pp. 537-547.
Seshadrinath, J., Singh, B., & Panigrahi, B. K. (2014). Incipient turn fault detection and condition monitoring of induction machine using analytical wavelet transform. IEEE Transactions on Industry Applications, vol. 50, no. 3, pp. 2235-2242.
De Angelo, C. H., Bossio, G. R., Giaccone, S. J., Valla, M. I., Solsona, J. A., & Garcia, G. O. (2009). Online model-based stator-fault detection and identification in induction motors. IEEE Transactions on Industrial Electronics, vol. 56, no. 11, pp. 4671-4680.
Tallam, R. M., Habetler, T. G., & Harley, R. G. (2002). Transient model for induction machines with stator winding turn faults. IEEE Transactions on Industry Applications, vol. 38, no. 3, 632-637.
Henao, H., Demian, C., & Capolino, G. A. (2003). A frequency-domain detection of stator winding faults in induction machines using an external flux sensor. IEEE Transactions on Industry Applications, vol. 39, no 5, pp. 1272-1279.
Lamim Filho, P. C. M., Pederiva, R., & Brito, J. N. (2014). Detection of stator winding faults in induction machines using flux and vibration analysis. Mechanical Systems and Signal Processing, vol. 42, no. 1, pp. 377-387.
Jin, C., Ompusunggu, A. P., Liu, Z., Ardakani, H. D., Petré, F., & Lee, J. (2015). Envelope analysis on vibration signals for stator winding fault early detection in 3-phase induction motors. International Journal of Prognostics and Health Management.
Kallesoe, C. S., Vadstrup, P., Rasmussen, H., & Izadi-Zamanabadi, R. (2004). Estimation of stator winding faults in induction motors using an adaptive observer scheme. Conference Record of the IEEE IAS Annual Meeting, vol. 2, pp. 1225-1232.
Bachir, S., Tnani, S., Trigeassou, J. C., & Champenois, G. (2006). Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Transactions on Industrial Electronics, vol. 53, no. 33, pp. 963-973.
Nguyen, V. H., Wang, D. W., Ukil, A., Nadarajan, S., Vaiyapuri, V., & Jayampathi, C. (2015). A closed-form solution to fault parameter estimation and faulty phase identification of stator winding inter-turn fault in induction machines. Annual Conference of the IEEE Industrial Electronics Society, pp. 286-291.
Nguyen, V. H., Wang, D. W., Seshadrinath, J., Nadarajan, S., & Vaiyapuri, V. (2016). Fault severity estimation using nonlinear Kalman filter for induction motors under inter-turn fault. Annual Conference of the IEEE Industrial Electronics Society, pp. 1488-1493.
Orchard, M. E., & Vachtsevanos, G. J. (2009). A particlefiltering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, vol. 31 no. 3-4, pp. 221-246.
Zio, E., & Peloni, G. (2011). Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering & System Safety, vol. 96, no. 3, pp. 403-409.
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on signal processing, vol. 50, no. 2, pp. 174-188.
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and computing, vol. 10, no. 3, pp. 197-208.
Pitt, M. K., & Shephard, N. (1999). Filtering via simulation: Auxiliary particle filters. Journal of the American statistical association, vol. 94, no. 446, pp. 590-599.
Musso, C., Oudjane, N., & Le Gland, F. (2001). Improving regularized particle filters. In Sequential Monte Carlo Methods in Practice, A. Doucet, N. De Freitas, and N.
Gordon, Eds. New York: Springer. Gordon, N. J., Salmond, D. J., & Smith, A. F. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing) , vol. 140, no. 2, pp. 107-113.
Braun, M. (1975). Differential equations and their applications, Springer-Verlag, 1975.
Chen, W., (1991). Nonlinear Analysis of Electronic Prognostics. Doctoral dissertation. The Technical University of Napoli, Napoli, Italy.
Ferrell, B. L. (1999), JSF Prognostics and Health Management. Proceedings of IEEE Aerospace Conference. March 6-13, Big Sky, MO. doi: 10.1109/AERO.1999.793190
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, vol. 22, pp. 679-688. doi:10.1016/j.ijforecast.2006.03.001
International Standards Organization (ISO) (2004). Condition Monitoring and Diagnostics of Machines - Prognostics part 1: General Guidelines. In ISO, ISO13381-1:2004(e). vol. ISO/IEC Directives Part 2, I. O. f. S. (ISO), (p. 14). Genève, Switzerland: International Standards Organization.
Schwabacher, M., & Goebel, K. F. (2007). A survey of artificial intelligence for prognostics. Proceedings of AAAI Fall Symposium, November 9–11, Arlington, VA. www.aaai.org/Library/Symposia/Fall/2007/fs07-02-016.php
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering system. Hoboken, NJ: John Wiley & Sons, Inc
Arkan, M., Perovic, D. K., & Unsworth, P. (2001). Online stator fault diagnosis in induction motors. IEE Proceedings-Electric Power Applications, vol. 148, no. 6, pp. 537-547.
Seshadrinath, J., Singh, B., & Panigrahi, B. K. (2014). Incipient turn fault detection and condition monitoring of induction machine using analytical wavelet transform. IEEE Transactions on Industry Applications, vol. 50, no. 3, pp. 2235-2242.
De Angelo, C. H., Bossio, G. R., Giaccone, S. J., Valla, M. I., Solsona, J. A., & Garcia, G. O. (2009). Online model-based stator-fault detection and identification in induction motors. IEEE Transactions on Industrial Electronics, vol. 56, no. 11, pp. 4671-4680.
Tallam, R. M., Habetler, T. G., & Harley, R. G. (2002). Transient model for induction machines with stator winding turn faults. IEEE Transactions on Industry Applications, vol. 38, no. 3, 632-637.
Henao, H., Demian, C., & Capolino, G. A. (2003). A frequency-domain detection of stator winding faults in induction machines using an external flux sensor. IEEE Transactions on Industry Applications, vol. 39, no 5, pp. 1272-1279.
Lamim Filho, P. C. M., Pederiva, R., & Brito, J. N. (2014). Detection of stator winding faults in induction machines using flux and vibration analysis. Mechanical Systems and Signal Processing, vol. 42, no. 1, pp. 377-387.
Jin, C., Ompusunggu, A. P., Liu, Z., Ardakani, H. D., Petré, F., & Lee, J. (2015). Envelope analysis on vibration signals for stator winding fault early detection in 3-phase induction motors. International Journal of Prognostics and Health Management.
Kallesoe, C. S., Vadstrup, P., Rasmussen, H., & Izadi-Zamanabadi, R. (2004). Estimation of stator winding faults in induction motors using an adaptive observer scheme. Conference Record of the IEEE IAS Annual Meeting, vol. 2, pp. 1225-1232.
Bachir, S., Tnani, S., Trigeassou, J. C., & Champenois, G. (2006). Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Transactions on Industrial Electronics, vol. 53, no. 33, pp. 963-973.
Nguyen, V. H., Wang, D. W., Ukil, A., Nadarajan, S., Vaiyapuri, V., & Jayampathi, C. (2015). A closed-form solution to fault parameter estimation and faulty phase identification of stator winding inter-turn fault in induction machines. Annual Conference of the IEEE Industrial Electronics Society, pp. 286-291.
Nguyen, V. H., Wang, D. W., Seshadrinath, J., Nadarajan, S., & Vaiyapuri, V. (2016). Fault severity estimation using nonlinear Kalman filter for induction motors under inter-turn fault. Annual Conference of the IEEE Industrial Electronics Society, pp. 1488-1493.
Orchard, M. E., & Vachtsevanos, G. J. (2009). A particlefiltering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, vol. 31 no. 3-4, pp. 221-246.
Zio, E., & Peloni, G. (2011). Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering & System Safety, vol. 96, no. 3, pp. 403-409.
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on signal processing, vol. 50, no. 2, pp. 174-188.
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and computing, vol. 10, no. 3, pp. 197-208.
Pitt, M. K., & Shephard, N. (1999). Filtering via simulation: Auxiliary particle filters. Journal of the American statistical association, vol. 94, no. 446, pp. 590-599.
Musso, C., Oudjane, N., & Le Gland, F. (2001). Improving regularized particle filters. In Sequential Monte Carlo Methods in Practice, A. Doucet, N. De Freitas, and N.
Gordon, Eds. New York: Springer. Gordon, N. J., Salmond, D. J., & Smith, A. F. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing) , vol. 140, no. 2, pp. 107-113.
Braun, M. (1975). Differential equations and their applications, Springer-Verlag, 1975.
Chen, W., (1991). Nonlinear Analysis of Electronic Prognostics. Doctoral dissertation. The Technical University of Napoli, Napoli, Italy.
Ferrell, B. L. (1999), JSF Prognostics and Health Management. Proceedings of IEEE Aerospace Conference. March 6-13, Big Sky, MO. doi: 10.1109/AERO.1999.793190
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, vol. 22, pp. 679-688. doi:10.1016/j.ijforecast.2006.03.001
International Standards Organization (ISO) (2004). Condition Monitoring and Diagnostics of Machines - Prognostics part 1: General Guidelines. In ISO, ISO13381-1:2004(e). vol. ISO/IEC Directives Part 2, I. O. f. S. (ISO), (p. 14). Genève, Switzerland: International Standards Organization.
Schwabacher, M., & Goebel, K. F. (2007). A survey of artificial intelligence for prognostics. Proceedings of AAAI Fall Symposium, November 9–11, Arlington, VA. www.aaai.org/Library/Symposia/Fall/2007/fs07-02-016.php
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering system. Hoboken, NJ: John Wiley & Sons, Inc
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