Autonomous, Data-Driven Fault Detection and Management System for Spacecraft

Back to all technologies
Download as PDF
Researchers at Purdue University have developed a dynamic neural network-based fault detection, isolation and recovery (FDIR) system which demonstrates reduced false positives and more robust determination of root cause. Increasing numbers and complexity of small spacecraft and missions demands improved autonomy for FDIR. Traditional rule-based methods such as limit checking possess limit ability to perform onboard diagnosis. Data-driven approaches based on data mining of telemetry have emerged out of the field of machine learning and provide more capable and informative ways of detecting faults, but many of these algorithms are susceptible to a high rate of false positives. The system has been validated against several common methods in a spacecraft simulator.

-High computational efficiency
-Reduced rates of false positives
-Robust determination of root cause
-Allows spacecraft to return to the desired operational state without ground intervention

-Small satellites
-Other spacecraft
Nov 7, 2019
United States

Nov 7, 2019
United States
Purdue Office of Technology Commercialization
The Convergence Center
101 Foundry Drive, Suite 2500
West Lafayette, IN 47906

Phone: (765) 588-3475
Fax: (765) 463-3486