A generalized least-squares fault detection filter

A fault detection and identification algorithm is determined from a general ization of the least-squares derivation of the Kalman filter. The objective of the filter is to monitor a single fault called the target fault and blo ck other faults which are called nuisance faults. The filter is derived fro m solving a min-max problem with a generalized least-squares cost criterion which explicitly makes the residual sensitive to the target fault, but ins ensitive to the nuisance faults. It is shown that this filter approximates the properties of the classical fault detection filter such that in the lim it where the weighting on the nuisance faults is zero, the generalized leas t-squares fault detection filter becomes equivalent to the unknown input ob server where there exists a reduced-order filter. Filter designs can be obt ained for both linear time-invariant and time-varying systems. Copyright (C ) 2000 John Wiley & Sons, Ltd.