Model-based approach to rover health assessment for increased productivity

Abstract

This paper presents an analysis tool useful for assessing diagnostic performance of a model-based fault management (FM) system. The FM system called MONSID is designed to provide off-nominal state detection and identification capabilities that are key components to assessing spacecraft state awareness. The analysis tool can be applied to MONSID models to predict MONSID's diagnostic performance for various sensor suite configurations and model topologies. The underlying algorithms of the diagnostic resolution analysis tool are discussed. The tool is applied to a MONSID model of a robot power subsystem to illustrate how MONSID's ability to distinguish among potentially faulty components is affected by the number of sensors and their placement (injection points) in the model. The tool can be utilized for FM design early in the program by supporting sensor suite selection. In the operations phase, it can be used to reduce the amount of onboard processing required by the MONSID engine in the fault identification process.