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 language | English (US) |
---|---|
Title of host publication | Dynamic Systems and Control |
Subtitle of host publication | Volume 1 |
Publisher | American Society of Mechanical Engineers (ASME) |
Pages | 195-202 |
Number of pages | 8 |
ISBN (Electronic) | 9780791826645 |
DOIs | |
State | Published - 2000 |
Externally published | Yes |
Event | ASME 2000 International Mechanical Engineering Congress and Exposition, IMECE 2000 - Orlando, United States Duration: Nov 5 2000 → Nov 10 2000 |
Publication series
Name | ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) |
---|---|
Volume | 2000-N |
Conference
Conference | ASME 2000 International Mechanical Engineering Congress and Exposition, IMECE 2000 |
---|---|
Country/Territory | United States |
City | Orlando |
Period | 11/5/00 → 11/10/00 |
Bibliographical note
Publisher Copyright:© 2000 by ASME