Bayesian network model with dynamic structure identification for real time diagnosis

This paper proposes a method for real time diagnosis against product quality drifts in an automated manufacturing system. We use Logical Diagnosis model to reduce the search space of suspected equipment in the production flow, which is then formulated as a Bayesian network to compute risk priority for each equipment, using joint and conditional probabilities. The objective is to quickly and accurately localize the possible fault origins and support effective decisions on corrective maintenance. The key advantages offered by this method are (i) reduced unscheduled equipment breakdowns, and (ii) increased and stable production capacities, required for success in highly competitive and automated manufacturing systems. Moreover, this is a generic method and can be deployed on fully or semi automated manufacturing systems.