Prognostics CoE Publications

This paper demonstrates a novel optimizationbased
approach to estimating fault states in a DC power
system. The model includes faults changing the circuit topology
along with sensor faults. Our approach can be considered as
a relaxation of the mixed estimation problem. We develop
a linear model of the circuit and pose a convex problem
for estimating the faults and other hidden states. A sparse
fault vector solution is computed by using l1 regularization.
The solution is computed reliably and efficiently, and gives
accurate diagnostics on the faults. We demonstrate a real-time
implementation of the approach for an instrumented electrical
power system testbed at NASA. Accurate estimates of multiple
faults are computed in milliseconds on a PC. The approach
performs well despite unmodeled transients and other modeling
uncertainties present in the system.