Title :
Hierarchical fault diagnosis and health monitoring in multi-platform space systems
Author :
Barua, A. ; Khorasani, K.
Author_Institution :
Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, QC
Abstract :
Current spacecraft health monitoring and fault diagnosis practices that involve around-the-clock limit-checking and trend analysis on large amount of telemetry data, do not scale well for future multi-platform space missions due to the presence of larger amount of telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the size of the operations team. The need for efficient utilization of telemetry data by employing machine learning and rule-based reasoning has been pointed out in the literature in order to enhance diagnostic performance and assist the less-experienced personnel in performing monitoring and diagnosis tasks. In this research we develop a systematic and transparent fault diagnosis methodology within a hierarchical fault diagnosis framework for multiplatform space systems. Our proposed Bayesian network-based hierarchical fault diagnosis methodology allows fuzzy rule-based reasoning at different components in the hierarchy. 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. Our proposed methodology is likely to enhance the level of autonomy in ground support based spacecraft health monitoring and fault diagnosis.
Keywords :
aerospace components; aerospace computing; condition monitoring; failure analysis; fault diagnosis; fuzzy reasoning; ground support systems; learning (artificial intelligence); space vehicles; telemetry; aerospace components; around-the-clock limit-checking analysis; fuzzy rule-based reasoning; ground support system; machine learning; multiplatform space system; spacecraft fault diagnosis methodology; spacecraft health monitoring; telemetry data; Bayesian methods; Condition monitoring; Fault diagnosis; Fuzzy reasoning; Ground support; Machine learning; Personnel; Space missions; Space vehicles; Telemetry;
Conference_Titel :
Aerospace conference, 2009 IEEE
Conference_Location :
Big Sky, MT
Print_ISBN :
978-1-4244-2621-8
Electronic_ISBN :
978-1-4244-2622-5
DOI :
10.1109/AERO.2009.4839690