A New Approach for Robust and Reduced Order Fault Detection Filter Design

The objective of this paper is to develop a practical methodology for designing full and reduced order fault detection filter for plants with polytopic model uncertainty. Polytopic models provide a very general framework for describing uncertainties in engineering applications. Because the polytopic model description is convex, it is amenable for a linear matrix inequality (LMI) formulation. Reduced order filters are desirable in applications where fast data processing is necessary. Robust fault detection filter (RFDF) design is formulated as a multi-objective H_infin optimization for a polytopic uncertain system. The order of the RFDF is reduced using LMI techniques and the detection performance is compared with the full order filter. An adaptive threshold is used to reduce the number of false alarms. An example is presented to illustrate effectiveness of the order reduction.