Failure Detection and Isolation using System Structure Knowledge and Eigenvalue Sensitivity

This paper presents the development of a model reference innovations based failure detection and isolation (FDI) scheme. Recently, there has been many researches carried out on failure detection schemes. However, isolation and identification of the failed components is a very important step in FDI and this paper emphasizes this aspect. Eigenvalue sensitivity analysis is introduced to develop an apriori rating for failed component isolation. This rating determines which failures are more influential to system behavior. Application of this FDI scheme to a simple fuel flow control system illustrates its feasibility on failure detection and how failed components can be ranked from least probable to most probable using eigenvalue sensitivity.