Dynamic probabilistic model-based expert system for fault diagnosis

The design and implementation of a probabilistic model-based fault diagnosis expert system is described in this paper. Possible cause and effect graph (PCEG) methodology, an enhanced signed-directed graph (SDG) approach, was used for qualitative modeling. A rule-based approach is proposed to transform the Bayesian belief network into an acyclic network dynamically during the diagnosis phase to allow simple on-line probability calculation in a belief network with causality loops. A dynamic time-delay methodology is also proposed to manage the possibility of phantom alarms, which are the consequences of process time-delays. The application to a pilot scale distillation column with communication with DCS environment is presented. Two sample runs were included to demonstrate the concept of dynamic causal network modification and time-delay management. (C) 2000 Elsevier Science Ltd. All rights reserved.