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System Health Management functionality is an essential part of any space launch system. Health management functionality is an integral part of mission reliability, since it is needed to verify the reliability before the mission starts. Health Management is also a key factor in life cycle cost reduction and in increasing system availability. The degree of coverage needed by the system and the degree of coverage made available at a reasonable cost are critical parameters of a successful design. These problems are not unique to the launch vehicle world. In particular, the Intelligent Vehicle Highway System, commercial aircraft systems, train systems, and many types of industrial production facilities require various degrees of system health management. In all of these applications, too, the designers must balance the benefits and costs of health management in order to optimize costs. The importance of an integrated system is emphasized. That is, we present the case for considering health management as an integral part of system design, rather than functionality to be added on at the end of the design process. The importance of maintaining the system viewpoint is discussed in making hardware and software tradeoffs and in arriving at design decisions. We describe an approach to determine the parameters to be monitored in any system health management application. This approach is based on Design of Experiments (DOE), prototyping, failure modes and effects analyses, cost modeling and discrete event simulation. The various computer-based tools that facilitate the approach are discussed. The approach described originally was used to develop a fault tolerant avionics architecture for launch vehicles that incorporated health management as an integral part of the system. Finally, we discuss generalizing the technique to apply it to other domains. Several illustrations are presented.

This paper examines the relationship between the microwave backscattering coefficient of a vegetation canopy, sigma (can, 0) and the canopy's leaf area index (LAI). The relationship is established through the development of one model for corn and sorghum and another for wheat. Both models are extensions of the cloud model of Attema and Ulaby (1978). Analysis of experimental data measured at 8.6, 13.0, 17.0, and 35.6 GHz indicates that most of the temporal variations of sigma (can, 0) can be accounted for through variations in green LAI alone, if the latter is greater than 0.5.

The relationship between the radar backscattering coefficient of a vegetation canopy, sigma(0) sub can, and the canopy's leaf area index (LAI) is examined. The relationship is established through the development of a model for corn and sorghum and another for wheat. Both models are extensions of the cloud model of Attema and Ulaby (1978). Analysis of experimental data measured at 8.6, 13.0, 17.0, and 35.6 GHz indicates that most of the temporal variations of sigma(0) sub can can be accounted for through variations in green LAI alone, if the latter is greater than 0.5.

A model to predict the radar backscattering coefficient from crops must include the geometry of the canopy. Radar and ground-truth data taken on wheat in 1979 indicate that the model must include contributions from the leaves, from the wheat head, and from the soil moisture. For sorghum and corn, radar and ground-truth data obtained in 1979 and 1980 support the necessity of a soil moisture term and a leaf water term. The Leaf Area Index (LAI) is an appropriate input for the leaf contribution to the radar response for wheat and sorghum, however the LAI generates less accurate values for the backscattering coefficient for corn. Also, the data for corn and sorghum illustrate the importance of the water contained in the stalks in estimating the radar response.