Ground-support based satellite health monitoring and fault diagnosis practices involve around-the- clock limit-checking and trend analysis on large amount of telemetry data. They do not scale well for future multi-platform space missions due to the size of the telemetry data and an increasing need to make the long-duration missions cost- effective by limiting the operations team personnel. To utilize telemetry data efficiently, and to assist the less-experienced personnel in perform- ing monitoring and diagnosis tasks, we have developed a hierarchical fault diagnosis methodology. The hierarchical decomposition is presented through a novel Bayesian Network (BN) whose structure is developed from the knowledge of component health state dependencies, and the parameters are obtained by a proposed methodology that utilizes both node fault diagnosis performance data and domain experts’ beliefs. Our proposed model development procedure reduces the demand for expert’s time in eliciting probabilities significantly, and our approach provides the ground personnel with an ability to perform diagnostic reasoning across a number of subsystems and components coherently. Due to the unavailability of real formation flight data, we demonstrate the effectiveness of our proposed methodology by using synthetic data of a leader-follower formation flight configuration. Although our proposed approach is developed from the satellite fault diagnosis perspective, it is generic and is targeted towards other types of cooperative fleet vehicle diagnosis problems.
How to Cite
fault diagnosis, applications: space
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