Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks

Authors:
Eric Wolbrecht;Bruce D'ambrosio;Robert Paasch;Doug Kirby
Affiliations:
Yamaha Watercraft, Knoxville, TN;Department of Computer Science, Oregon State University, Corvallis, OR;Department of Mechanical Engineering, Oregon State University, Corvallis, OR;Hewlett Packard, Corvallis, OR
Venue:
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Year:
2000

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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.