Observer-based and regression model-based detection of emerging faults in coal mills

Abstract

To improve the reliability of power plants it is important to detect fault as fast as possible. Since modeling of large scale systems is time consuming, it is interesting to compare a model-based method with data driven ones. In this chapter three different fault detection approaches are compared using an example of a coal mill, where a fault emerges. The compared methods are based on: an optimal unknown input observer, static and dynamic regression model-based detections. The conclusion on the comparison is that the observer-based scheme detects the fault 13 samples earlier than the dynamic regression model-based method, and that the static regression based method is not usable due to generation of far too much false detection.