  Sequential change diagnosis is the joint problem of detection and
identification of a sudden and unobservable change in the distribution of a
random sequence. In this problem, the common probability law of a sequence of
i.i.d. random variables suddenly changes at some disorder time to one of
finitely many alternatives. This disorder time marks the start of a new regime,
whose fingerprint is the new law of observations. Both the disorder time and
the identity of the new regime are unknown and unobservable. The objective is
to detect the regime-change as soon as possible, and, at the same time, to
determine its identity as accurately as possible. Prompt and correct diagnosis
is crucial for quick execution of the most appropriate measures in response to
the new regime, as in fault detection and isolation in industrial processes,
and target detection and identification in national defense. The problem is
formulated in a Bayesian framework. An optimal sequential decision strategy is
found, and an accurate numerical scheme is described for its implementation.
Geometrical properties of the optimal strategy are illustrated via numerical
examples. The traditional problems of Bayesian change-detection and Bayesian
sequential multi-hypothesis testing are solved as special cases. In addition, a
solution is obtained for the problem of detection and identification of
component failure(s) in a system with suspended animation.
