Model-Based Diagnostic Decision-Support System for Satellites

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 x 10(-3) which can be automatically reduced to 9.5x10(-4) by sending a single, automatically computed, telecommand, thus dramatically reducing the fault isolation time.