ABSTRACT
The integrated health management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. This chapter aims to develop an intelligent IHM that enhances components and system reliability and provides a postflight feedback for optimization of the next generation of aerospace systems. It discusses the strengths of the algorithm are illustrated by inferring parameters of the stochastic Lorenz system and comparing the results with those of earlier research and presents a number of applications of the method to the IHM of aerospace system. Bayesian inferential framework is formulated for development of the IHM system for in-flight structural health monitoring of composite materials. The chapter describes an application of the dynamical inference algorithm to a system formed by a sequence of three interconnected tanks that has been declared as a benchmark for fault detection and diagnosis in dynamical system.
