Session
Technical Session XII: Software
Abstract
Space missions produce value through the production of mission data products and services. In doing so, however, significant resources are expended in order to maintain system health and to manage anomalies as they occur. These tasks are costly in terms of the expertise, personnel, and time required to detect, diagnose and resolve problems. Our recent work in model-based reasoning (MBR) techniques has demonstrated the applicability of this technology to the small satellite domain. MBR uses fundamental design knowledge of a system in order to compute reasoning conjectures relating to the existence of symptoms, diagnosis estimates, and resolution control actions. In doing so, it provides a systematic and efficient framework for automated reasoning, which in turn can dramatically accelerate the analysis of anomalies with significantly improved results. In this paper, we describe the MBR approach to anomaly management and review our theoretical and algorithmic contributions to this field. We outline our software toolboxes that implement these algorithms, and we highlight the tools that are being developed to apply this software to real space systems. Finally, we review results of using this reasoning system for several small satellite missions, ranging from the student-built Sapphire microsatellite to the NASA GeneSat-1 spacecraft.
Presentation Slides
Model-Based Anomaly Management for Small Spacecraft Missions
Space missions produce value through the production of mission data products and services. In doing so, however, significant resources are expended in order to maintain system health and to manage anomalies as they occur. These tasks are costly in terms of the expertise, personnel, and time required to detect, diagnose and resolve problems. Our recent work in model-based reasoning (MBR) techniques has demonstrated the applicability of this technology to the small satellite domain. MBR uses fundamental design knowledge of a system in order to compute reasoning conjectures relating to the existence of symptoms, diagnosis estimates, and resolution control actions. In doing so, it provides a systematic and efficient framework for automated reasoning, which in turn can dramatically accelerate the analysis of anomalies with significantly improved results. In this paper, we describe the MBR approach to anomaly management and review our theoretical and algorithmic contributions to this field. We outline our software toolboxes that implement these algorithms, and we highlight the tools that are being developed to apply this software to real space systems. Finally, we review results of using this reasoning system for several small satellite missions, ranging from the student-built Sapphire microsatellite to the NASA GeneSat-1 spacecraft.