Intelligent Sensor
Intelligent Sensor Based Bayesian Neural Network for Combined Parameters and States Estimation of a Brushed DC Motor
Hacene MELLAH , Kamel Eddine HEMSAS, Rachid TALEB
Abstract: The objective of this paper is to develop an Artificial Neural Network (ANN) model to estimate simultaneously, parameters and state of a brushed DC machine. The proposed ANN estimator is novel in the sense that his estimates simultaneously temperature, speed and rotor resistance based only on the measurement of the voltage and current inputs. Many types of ANN estimators have been designed by a lot of researchers during the last two decades. Each type is designed for a specific application. The thermal behavior of the motor is very slow, which leads to large amounts of data sets. The standard ANN use often Multi-Layer Perceptron (MLP) with Levenberg-Marquardt Backpropagation (LMBP), among the limits of LMBP in the case of large number of data, so the use of MLP based on LMBP is no longer valid in our case. As solution, we propose the use of Cascade-Forward Neural Network (CFNN) based Bayesian Regulation backpropagation (BRBP). To test our estimator robustness a random white-Gaussian noise has been added to the sets. The proposed estimator is in our viewpoint accurate and robust.
Keywords
DC motor; thermal modeling; state and parameter estimations; Bayesian regulation; backpropagation; cascade-forward neural network
References
[1] P. P. Acarnley, J. K. Al-Tayie, Estimation of speed and armature temperature in a brushed DC drive using the extended Kalman filter, IEE Proc Electr. Power Appl., vol. 144, no. 1, pp. 13–20, Jan I997.
[2] E. Fiorucci, G. Bucci, F.Ciancetta, D. Gallo, C. Landi and M. Luiso, variable speed drive characterization: review of measurement techniques and future trends, Advances in Power Electronics, vol. 2013, pp.1–14, 2013.
[3] G. Bucci, C. Landi, Metrological characterization of a contactless smart thrust and speed sensor for linear induction motor testing, Instrumentation and Measurement, IEEE Transactions on , vol. 45, no.2, pp. 493 – 498, Apr 1996.
[4] R. J. Welch and G. W. Younkin, How Temperature Affects a Servomotor's Electrical and Mechanical Time Constants, Proc. IEEE Ind. Appl. Conference, vol. 2, pp. 1041–1046, 13-18 Oct. 2002.
[5] IEEE Recommended Practice for General Principles of Temperature Measurement as Applied to Electrical Apparatus, IEEE Std 119-1974,1974.
[6] T. Chunder, Temperature rise measurement in armature of a DC motor, under running conditions by telemetry, Proc. Sixth International Conference on Electrical Machines and Drives, pp. 44–48, 8-10 Sep 1993.
[7] L. Michalski, K. Eckersdorf, J. Kucharski, J. McGhee, Temperature Measurement, John Wiley & Sons Ltd, 2001.
[8] I. J. Aucamp, L .J. Grobler, Heating, ventilation and air conditioning management by means of indoor temperature measurements, Proc. 9th conference industrial and commercial use of energy (ICUE), pp. 1–4, 15-16 Aug, 2012.
[9] A. Cassat, C. Espanet and N. Wavre, BLDC Motor Stator and Rotor Iron Losses and Thermal Behavior Based on Lumped Schemes and 3-D FEM Analysis, IEEE Transactions on Industry Applications, vol. 39, no. 5, pp. 1314–1322, 2003.
[10] J. Le Besnerais, A. Fasquelle, M. Hecquet, J. Pellé, V. Lanfranchi, S. Harmand, P. Brochet and A. Randria, Multiphysics Modeling: Electro-Vibro-Acoustics and Heat Transfer of PWM-Fed Induction Machines, IEEE Transactions on Industrial Electronics, vol. 57, no. 4, pp. 1279–1287, 2010.
[11] R. Lazarevic, P. Radosavljevic, A. Osmokrovic, novel approach for temperature estimation in squirrel-cage induction motor without sensors, IEEE Transactions on Instrumentation and Measurement, vol. 48, no. 3, pp. 753–757, 1999.
[12] S. B. Lee, T. G. Habetler, R. G. Harley and D. J. Gritter, A stator and rotor resistance estimation technique for conductor temperature monitoring, Proc. IEEE Ind. Appl. Conference, vol. 1, pp. 381–387, 2000.
[13] S. B. Lee, T. G. Habetler, R. G. Harley and D. J. Gritter, An Evaluation of Model-Based Stator Resistance Estimation for Induction Motor Stator Winding Temperature Monitoring, IEEE Transactions on Energy Conversion, vol. 17, no. 1, pp. 7–15, 2002.
[14] S. B. Lee, T. G. Habetler, An Online Stator Winding Resistance Estimation Technique for Temperature Monitoring of Line-Connected Induction Machines, IEEE Transactions on Industry Applications, vol. 39, no. 3, pp. 685–694, 2003.
[15] K. D. Hurst, T.G. Habetler, A thermal monitoring and parameter tuning scheme for induction machines, Proc. IEEE Ind. Appl. Conference, IEEE-IAS Annu. Meeting, vol. 1, pp. 136–142, 1997.
[16] H. Mellah, K. E. Hemsas, Stochastic Estimation Methods for Induction Motor Transient Thermal Monitoring Under Non Linear Condition, Leonardo Journal of Sciences, vol. 11, pp. 95–108, 2012.
[17] J. F. Moreno, F. P. Hidalgo and M. D. Martinez, Realisation of tests to determine the parameters of the thermal model of an induction machine, IEE Proc Electr. Power Appl., vol. 148, no.5, pp. 393–397, 2001.
[18] R. Beguenane, M.E.H. Benbouzid, Induction motors thermal monitoring by means of rotor resistance identification, IEEE Transaction on Energy Conversion, vol. 14, no. 3, pp. 566-570, 1999.
[19] M.S.N. Saïd, M.E.H. Benbouzid, H–G Diagram Based Rotor Parameters Identification for Induction Motors Thermal Monitoring, IEEE Transactions on Energy Conversion, vol. 15, no. 1, pp. 14–18, 2000.
[20] Z. Gao, T. G. Habetler, R. G. Harley and R. S. Colby, An Adaptive Kalman Filtering Approach to Induction Machine Stator Winding Temperature Estimation Based on a Hybrid Thermal Model, Proc. IEEE Ind. Appl. Conference, IEEE-IAS Annu. Meeting, vol. 1, pp. 2–9, 2005.
[21] R. Pantonial, A. Kilantang and B. Buenaobra, Real time thermal estimation of a Brushed DC Motor by a steady-state Kalman filter algorithm in multi-rate sampling scheme, Proc TENCON 2012 IEEE Region 10 Conference, pp. 1–6, 19-22 Nov 2012.
[22] W. Zhang, S. G. Andrew and R.H. Saeid, Nonlinear Estimation of Stator Winding Resistance in a Brushless DC Motor, Proc American Control Conference (ACC), pp. 4699-4704, 17-19 June 2013.
[23] M. Jabri, I. Chouire and N.B. Braiek, Fuzzy Logic Parameter Estimation of an Electrical System, Proc. International Multi-Conference on Systems, Signals and Devices, pp.1–6, 2008.
[24] M. Jabri, A. Belgacem and Houssem Jerbi, Moving Horizon Parameter Estimation of Series Dc Motor Using Genetic Algorithm, Proc. International Multi-Conference on Systems, Signals and Devices, pp. 26–27, 2009.
[25] S. A. Kalogirou, Artificial neural networks in renewable energy systems applications: a review, Renewable and Sustainable Energy Reviews, vol. 5, no. 4, pp.373–401, 2001.
[26] E. Byvatov, U. Fechner, J. Sadowski and G. Schneider, Comparison of support vector machine and artificial neural network systems for drug/nondrug classification, Journal of Chemical and modeling, vol. 43, no. 6, pp. 1882–1889, 27 Sept, 2003
[27] S. Agatonovic-Kustrin, R. Beresford, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research,
Journal of pharmaceutical and Biomedical Analysis, vol. 22, no. 5, pp. 717–727, 2000.
[28] S. Ablameyko, L.Goras, M. Gorz and V. Piuri, Neural Networks for Instrumentation, Measurement and Related Industrial Applications, IOS Press, 2003.
[29] S. Haykin, Kalman filtering and neural networks, John Wiley & Sons, 2001.
[30] A. Cochocki, R. Unbehauen, Neural networks for optimization and signal processing. John Wiley & Sons, Inc, 1993.
[31] M. Y. Chow, Y. Tipsuwan, Neural plug-in motor coil thermal modeling, in Industrial Electronics Society, 2000. IECON 2000. 26th Annual Conference of the IEEE, vol.3, no., pp.1586–1591, 2000.
[32] L. P. Veelenturf, Analysis and applications of artificial neural networks, Prentice-Hall, Inc., 1995.
[33] M. Gupta, L. Jin and N. Homma, Static and dynamic neural networks: from fundamentals to advanced theory, John Wiley & Sons, 2004.
[34] L. C. Jain, N.M. Martin, Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications, vol. 4, CRC press. 1998.
[35] R. C. Eberhart, J. Kennedy, A New Optimizer Using Particle Swarm Theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS '95, vol.1, pp. 39–43. 1995.
[36] J.S.R. Jang, C.T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, 1997.
[37] C. Dimoulas, G. Kalliris, G. Papanikolaou, V. Petridis and A. Kalampakas, Bowel-sound pattern analysis using wavelets and neural networks with application to long-term, unsupervised, Expert Systems with Applications, vol. 34, no. 1, pp. 26–41, 2008.
[38] B.M. Wilamowski, How to not get frustrated with neural networks, Proc. IEEE Int. Conf. Ind. Technol, pp. 5–11., 2011.
[39] Zhou Yao-ming, Meng Zhi-jun, Chen Xu-zhi and Wu Zhe, Helicopter Engine Performance Prediction based on Cascade-Forward Process Neural Network, IEEE Conference on Prognostics and Health Management (PHM), pp, 1–5, 18-21 June 2012.
[40] H. Demuth, M. Beale and M. Hagan, Neural Network Toolbox Users Guide, the MathWorks, Natrick, USA. 2009.
Neghab, H. K., & Neghab, H. K. (2021). Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques. Periodica Polytechnica Electrical Engineering and Computer Science.
Moulik, S., & Halder, B. (2021). Model-Based Observer Performance Study for Speed Estimation of Brushed DC Motor with Uncertain Contact Resistance. In Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications (pp. 441-452). Springer, Singapore.
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