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Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks

Published online by Cambridge University Press:  01 January 2000

ERIC WOLBRECHT ,
BRUCE D'AMBROSIO ,
ROBERT PAASCH  and
DOUG KIRBY
ERIC WOLBRECHT
Affiliation:
Yamaha Watercraft, Knoxville, TN
BRUCE D'AMBROSIO
Affiliation:
Department of Computer Science, Oregon State University, Corvallis, OR
ROBERT PAASCH
Affiliation:
Department of Mechanical Engineering, Oregon State University, Corvallis, OR
DOUG KIRBY
Affiliation:
Hewlett Packard, Corvallis, OR
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Abstract

The application of Bayesian networks for monitoring and diagnosis of a multistage manufacturing process is described. Bayesian network “part models” were designed to represent individual parts in-process. These were combined to form a “process model,” a Bayesian network model of the entire manufacturing process. An efficient procedure is designed for managing the “process network.” Simulated data is used to test the validity of diagnosis made from this method. In addition, a critical analysis of this method is given, including computation speed concerns, accuracy of results, and ease of implementation. Finally, a discussion on future research in the area is given.

Keywords

Bayesian NetworkProcess MonitoringProcess Diagnosis
Type
Research Article
Information
AI EDAM , Volume 14 , Issue 1 , January 2000 , pp. 53 - 67
Copyright
© 2000 Cambridge University Press

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