Formation flying is an emerging area in Earth and space science domain that utilizes multiple inexpensive spacecraft by distributing the functionalities of a single platform among miniature inexpensive platforms. Traditional spacecraft fault diagnosis and health monitoring practices that involve around-the-clock monitoring, threshold checking, and trend analysis of a large amount of telemetry data by human experts do not scale well for multiple space platforms. In this paper, a multi-level fault diagnosis methodology utilizing fuzzy rule-based reasoning is presented to enhance the level of autonomy in fault diagnosis at the ground stations. Effectiveness of the proposed fault diagnosis methodology is demonstrated by utilizing synthetic formation flying attitude control subsystem data. The proposed scheme has potential to serve as a prognostic tool when designed based on multiple fault severities, and hence can contribute in the overall health management process.
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