Modeling Uncertainty in System-Level Fault Diagnosis Using Process Graphs

This paper presents a novel approach to solving the probabilistic diagnosis problem in multiprocessor systems. The main idea of the algorithm is based on the reformulation of the error propagation model as a Process Network Synthesis (PNS) problem. This idea is illustrated by deriving a maximum likelihood decision procedure. The diagnostic accuracy of the solution is considered on the basis of simulation measurements, and a method of constructing a general framework for different aspects of a complex problem with the use of P-Graph models is demonstrated.