Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks

被引：39

作者：

Wolbrecht, Eric ^{[1
]}

D'Ambrosio, Bruce ^{[2
]}

Paasch, Robert ^{[3
,5
]}

Kirby, Doug ^{[4
]}

机构：

[1] Yamaha Watercraft, Knoxville, TN

[2] Department of Computer Science, Oregon State University, Corvallis, OR

[3] Department of Mechanical Engineering, Oregon State University, Corvallis, OR

[4] Hewlett Packard, Corvallis, OR

[5] Department of Mechanical Engineering, 204 Rogers Hall, Oregon State University, Corvallis

来源：

Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM | 2000年 / 14卷 / 01期

关键词：

Computer simulation - Computer systems programming;

D O I：

10.1017/S0890060400141058

中图分类号：

学科分类号：

摘要：

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.

引用

页码：53 / 67

页数：14

共 13 条

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