Stiction identification in nonlinear process control loops

Nearly 20-30% of all process control loops oscillate due to stiction and lead to loss of productivity. Thus, the detection and quantification of stiction in control valves using just the raw operating data is an important component of any automated controller performance monitoring application. Many techniques have been proposed for the detection and quantification of stiction. Pattern based identification approaches use unique shapes of the PV and OP data to identify stiction. Other approaches that include some measure of nonlinearity index have also been used to identify stiction. A solution technique for stiction detection in nonlinear processes with known process models is also available. In this paper, one possible approach to detect stiction in nonlinear process control loops with unknown process models is discussed.