Observer-based and Regression Model-based Detection of Emerging Faults in Coal Mills

In this paper three different approaches for fault detections are compared on example with coal mill used at a power plant. The compared methods are based on: an optimal unknown input observer, static and dynamic regression model-based detections. These approaches are compared on data from a coal mill, where a fault emerges during the test set. The conclusion on the comparison is that observerbased 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 many false detections.