New Advances in Sequential Diagnosis

Abstract

Sequential diagnosis takes measurements of an abnormal system to identify faulty components, where the goal is to reduce the diagnostic cost, defined here as the number of measurements. To propose measurement points, previous work employs a heuristic based on reducing the entropy over a set of diagnoses, which can be impractical when the set of diagnoses is too large. Focusing on a smaller set of probable diagnoses scales the approach but generally leads to increased diagnostic cost. We propose a new diagnostic framework employing three new techniques-a more efficient heuristic for measurement point selection, abstraction-based sequential diagnosis, and component cloning-which scales to large systems with good performance in terms of diagnostic cost.