TY - CHAP
T1 - Model-Based Diagnostic decision-support system for satellites
AU - Feldman, Alexander
AU - De Castro, Helena Vicente
AU - Van Gemund, Arjan
AU - Provan, Gregory
PY - 2013
Y1 - 2013
N2 - We propose a novel framework for Model-Based Diagnosis (MBD) that uses active testing to decrease the diagnostic uncertainty. This framework is called LYDIA-NG and combines several diagnostic, simulation, and active-testing algorithms. We have illustrated the workings of LYDIA-NG by building a LYDIA-NG-based decision support system for the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite. This paper discusses a model of the GOCE Electrical Power System (EPS), the algorithms for diagnosis and disambiguation, and the experiments performed with a number of diagnostic scenarios. Our experiments produced no false positive scenarios, no false negative scenarios, the average number of classification errors per scenario is 1.25, and the fault detection time is equal to the computation time. We have further computed an average fault uncertainty of 2.06 × 10-3 which can be automatically reduced to 9.5×10-4 by sending a single, automatically computed, telecommand, thus dramatically reducing the fault isolation time.
AB - We propose a novel framework for Model-Based Diagnosis (MBD) that uses active testing to decrease the diagnostic uncertainty. This framework is called LYDIA-NG and combines several diagnostic, simulation, and active-testing algorithms. We have illustrated the workings of LYDIA-NG by building a LYDIA-NG-based decision support system for the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite. This paper discusses a model of the GOCE Electrical Power System (EPS), the algorithms for diagnosis and disambiguation, and the experiments performed with a number of diagnostic scenarios. Our experiments produced no false positive scenarios, no false negative scenarios, the average number of classification errors per scenario is 1.25, and the fault detection time is equal to the computation time. We have further computed an average fault uncertainty of 2.06 × 10-3 which can be automatically reduced to 9.5×10-4 by sending a single, automatically computed, telecommand, thus dramatically reducing the fault isolation time.
UR - https://www.scopus.com/pages/publications/84878739282
U2 - 10.1109/AERO.2013.6497427
DO - 10.1109/AERO.2013.6497427
M3 - Chapter
AN - SCOPUS:84878739282
SN - 9781467318112
T3 - IEEE Aerospace Conference Proceedings
BT - 2013 IEEE Aerospace Conference, AERO 2013
T2 - 2013 IEEE Aerospace Conference, AERO 2013
Y2 - 2 March 2013 through 9 March 2013
ER -