Fault detection in lambda-tuned control loops

Sammanfattning: Poorly operating control loops cause loss in productivity in almost every industry worldwide. Therefore performance monitoring has been an active area of research for the past decades. In this work, a newly developed fault detection method is applied to the monitoring of lambda-tuned control loops. The lambda-tuning method has, due to its simple use, become very popular in the pulp and paper industry and is now spreading to other industries. A model-based fault detection algorithm assuming uncertain process parameters is used for detecting changes in the process. The algorithm consists of two parts, a residual and a time-varying threshold. The a priori information obtained from lambda-tuning is used to create an observer, which is used as residual generator. Known process inputs are used together with upper bounds for the uncertainties and upper bounds for disturbances when calculating the detection threshold. The observer may have integral action in order to make the threshold tight to the residual. Upper bounds for the uncertainties in the process parameters and upper bounds for disturbances are tuning parameters in the algorithm. Two different methods for finding those parameter values are proposed. The first is an approach based on allowed loss in phase margin and the second consists of solving a nonlinear optimization problem to minimize the difference between the residual and the threshold. The algorithm is tested in a simple water tank system. The threshold handles step changes in reference value without giving any false alarms. A fault is introduced by widening of the output in the water tank which simulates a change in the process parameters. The residual then gets larger than the threshold and the fault is detected.