Optimal fault detection design via iterative estimation methods for industrial control systems

This paper mainly focuses on the design and implementation of iterative estimation methods, and their applications to obtain the optimal fault detection for industrial control systems. More specifically, to generate residual signals with a minimum variance, minimum variance estimation is first addressed in terms of recursive least square (RLS) and Kalman filter by iterative interactions with the process environment. The optimal fault detection is then realized to provide the timely and optimal detection of potential problems by adopting these real-time minimum variance estimation schemes. Finally, the effectiveness of our schemes is demonstrated with a numerical example and experimental studies in the laboratory three-tank system.