State Estimation and Sensor Bias Detection Using Adaptive Linear Kalman Filter
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For safe and economical process operations, state estimation of dynamic systems is an important prerequisite, since all the process variables are generally not measurable. Keeping this in view, a linear and non-linear observer has been developed to estimate the state of a noisy dynamic system. Also chemical processes have been suffering from several faults since many decades. Finding the system faults and isolating them is essential because if a fault occurs in the plant, there will be serious economic losses due to drop in productivity. To serve this purpose, an Adaptive Linear Kalman Filter (ALKF) and an Extended Kalman Filter (EKF) have been designed to detect and isolate the faults. Of all the faults, sensor faults are taken into consideration in this paper. Our task is to compare the results of two estimators and to identify the better one, in estimating the state of non-linear system along with bias detection. Simulation studies are also included to estimate the residuals of the estimators.
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R. Isermann, Process fault detection based on Modelling and estimation Methods: A survey, Automatica, Vol. 20, 1984, pp 387-404.
Paul M. Frank, Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge-based Redundancy – A Survey and Some New Results, Automatica, Vol.26, No. 3, 1990, pp. 459-474.
R. J. Patton and J. Chen, Observer-Based Fault Detection and Isolation: Robustness and Applications, Control Engineering Practice, Vol.5, No.5, 1997, pp 671 – 682
Y. Soufi, T. Bahi, M. F. Harkat, M. Mohammedi , Fault Diagnosis Methods for Three Phase PWM Inverter Fed Induction Motor, International Review on Modelling and simulation, Vol. 2. n. 6, Dec 2009 , pp. 639-644.
Basseville, M.; Nikiforov, I. V, Detection of Abrupt Changes Theory and Applications, (Prentice Hall Information and Systems Sciences Series: New York, 1993).
Majed Jabri, Houssem Jerbi, Naceur Benhadj Braiek , Fault Detection of Synchronous Generator Based on Moving Horizon Parameter Estimation and Evolutionary Computation, International Review on Modelling and simulation, Vol. 3. n. 1, Feb 2010, pp. 38-47
Gertler, J. J, Fault Detection and Diagnosis in Engineering Systems, (Marcel Dekker: New York, 1998).
Peter S Maybeck, Stochastic models,estimaton and control, Academic Press, Inc , London 1979
Simon J.Julier, Jeffrey K.Uhlmann, Unscented filtering and nonlinear estimation, proceedings of the IEEE vol.92, No.3, March 2004, pp401 – 422.
D. Danielle and D. Cooper, A Practical Multiple Model Adaptive Strategy for Multivariable Model Predictive Control, Control Engineering Practice, vol 11, 2003, pp.649-664.
Anjali P. Deshpande, and Sachin C. Patwardhan, Online Fault Diagnosis in Nonlinear Systems Using the Multiple Operating Regime Approach, Industrial Engineering Chemistry Research, 47, 2008, pp 6711–6726
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