An inference design for fault location in real-time control systems

Abstract

We develop a probabilistic comparison-based model and fault-location strategies using statistical inference, namely point and set estimation. Our heuristic distance-based algorithm can efficiently locate the faults with a 1 — α level of confidence. We show how the exponential complexity of the global estimation process for fault location in a real-time system S is simplified using this approach. The complexity of the run-time fault location is O( n) for systems with n units. Fault-free systems can be handled, and there is a high likelihood of distinguishing truly faulty units from those that appear faulty because of the imperfect environment, thus eliminating unnecessary replacements during reconfiguration. Our work is intended to be a practical approach to fault location; it accommodates all the random effects of testing real-time control systems in a general way.