Efficient recursive algorithms for detection of abrupt changes in signals and control systems

This paper addresses a number of open problems concerning the generalized l ikelihood ratio (GLR) rules for online detection of faults and parameter ch anges in control systems. It is shown that with an appropriate choice of th e threshold and window size, these GLR rules are asymptotically optimal, Th e rules are also extended to nonlikelihood statistics that are widely used in monitoring adaptive algorithms for system identification and control by establishing Gaussian approximations to these statistics when the window si ze is chosen suitably. Recursive algorithms are developed for practical imp lementation of the procedure, and importance sampling techniques are introd uced for determining the threshold of the rule to satisfy prescribed bounds on the false alarm rate.