Bayesian Belief Network Modeling and Diagnosis of Xerographic Systems

Abstract

Abstract In this paper, a Bayesian Belief Network (BBN) approach to the modeling and diagnosis of xerographic printing systems is proposed. First, a continuous BBN model based on physics of the printing process and field data is developed. The model captures the causal relationships between the various physical variables in the system using conditional probability distributions. Next, the continuous BBN is discretized based on the principle of maximum entropy so that it can be implemented on commercially available software, Hugin. The resulting BBN can be used for the prediction of print quality behaviors, as well as for inference and fault diagnosis. Examples of network deduction and inference are presented to illustrate the usefulness of the BBN model.