A New Real-Time Automated Ground Health Monitoring System at a Satellite Ground Control Station

R W. Johnson and S. Jayaram


High Fidelity Dynamic Modeling (HFDM), real-time, detection/diagnosis, Extended Kalman filter (EKF), Satellite Ground Control Station (SGCS)


A new real-time detection/diagnosis methodology for an Automated Ground Health Monitoring System (AGHMS) is applied at a satellite ground control station. The technological innovations in this research are focused on the identification of abnormal transient response profiles from a satellite 6-DOF attitude control platform. The identification will be made by comparing, in real time, the filtered (Kalman) measurements to a synchronized model reference system. The methodology used to accomplish this task will be software intensive and perfectly compatible with the open physical architecture of existing monitoring devices and their automated control system mechanisms. The innovations demonstrated will be (1) a real-time Extended Kalman filter to eliminate the measurement noise; (2) the formulation and use of a "dynamic threshold" detection system to identify abnormal "state estimates" as well as "covariance estimates"; and (3) the generation of an intelligent fault-mode file with corrective control commands to stabilize mild detected faults. The objective of this article is to provide these technical enhancements by handling and evaluating test data differently. An example is included to demonstrate these technical innovations. The AGHMS methodology will demonstrate real-time signal detection using an Extended Kalman filter (EKF) to obtain the best estimate of measurements (i.e., the dynamic parameters of a system); to obtain precise knowledge of the attitude of a satellite or a spacecraft that has an onboard magnetometer for attitude measurements; to study and analyze the state co-variance, as well as the error co-variance of the system; and to enhance the processing capability by monitoring systems in real time, perform systems detection/diagnosis, and actively control the environment/process based on these onboard sensor readings.

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