New Advances in Sequential Diagnosis

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.