Bayesian belief network modeling and diagnosis of xerographic systems

Chunhui Zhong, Perry Y. Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationDynamic Systems and Control
Subtitle of host publicationVolume 1
PublisherAmerican Society of Mechanical Engineers (ASME)
Pages195-202
Number of pages8
ISBN (Electronic)9780791826645
DOIs
StatePublished - 2000
EventASME 2000 International Mechanical Engineering Congress and Exposition, IMECE 2000 - Orlando, United States
Duration: Nov 5 2000Nov 10 2000

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume2000-N

Conference

ConferenceASME 2000 International Mechanical Engineering Congress and Exposition, IMECE 2000
Country/TerritoryUnited States
CityOrlando
Period11/5/0011/10/00

Bibliographical note

Publisher Copyright:
© 2000 by ASME

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