Abstract—Robust fault detection filter (RFDF) is mainly designed for detecting faults in linear time-invariant (LTI) systems inherently exposed to external disturbance and noise. One of the methods used for RFDF is Kalman approach. The estimation error of continuous Kalman filter (CKF) for the continuous original model is lower than discrete and continuous-discrete Kalman filters. Furthermore, discretizing the continuous model may cause losing some information. The fundamental purpose of our study is to introduce a new reference residual model generator to formulate the RFDF based on a CKF model through which the generated residual signal can be evaluated and then applying it in a drum boiler in Synvendska Kraft AB Plant in Malmo, Sweden as a multivariable and strongly coupled system. We hypothesize the proposed design is more explicit and more accurate with lower estimation error than other Kalman approaches. To the best of our knowledge, this is for the first time that a CKF model is employed to detect actuator and sensor faults and studied in a real boiler. It is demonstrated that both sensor and actuator faults can robustly be detected. Also sensor faults can be isolated through this approach.
Index Terms—Boiler, continuous Kalman filter, fault detection, robustness.
L. Khoshnevisan was with the Electrical and Computer Engineering
department of Tarbiat Modares University, Tehran, Iran. She is now with the
Electrical Engineering Department of the University of Tehran, Tehran, Iran.
(e-mail: firstname.lastname@example.org ; email@example.com).
H. R. Momeni is with the Electrical and Computer Engineering department of Tarbiat Modares University, Tehran, Iran (e-mail: firstname.lastname@example.org).
A. Ashraf-Modarres is with the Research Department of MAPNA Company, Tehran, Iran. (e-mail: email@example.com).
Cite: Ladan Khoshnevisan, Hamid Reza Momeni, and Ali Ashraf-Modarres, "Introduction of a Robust Fault Detection Filter Model Based on Continuous Kalman Filter for a Real Drum Boiler System," International Journal of Modeling and Optimization vol. 2, no. 4, pp. 539-543, 2012.