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Learning and Diagnosis in Manufacturing Processes through an Executable Bayesian Network

  • M. A. Rodrigues
  • Y. Liu
  • L. Bottaci
  • D. I. Rigas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1821)

Abstract

In this paper we present a novel approach to modelling a manufacturing process that allows one to learn about causal mechanisms of manufacturing defects through a Process Modelling and Executable Bayesian Network (PMEBN). The method combines probabilistic reasoning with time dependent parameters which are of crucial interest to quality control in manufacturing environments. We demonstrate the concept through a case study of a caravan manufacturing line using inspection data.

Keywords

Bayesian Network Joint Probability Distribution Representation Framework Component Code Time Dependent Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • M. A. Rodrigues
    • 1
  • Y. Liu
    • 2
  • L. Bottaci
    • 2
  • D. I. Rigas
    • 2
  1. 1.School of Computing & ManagementSheffield Hallam UniversitySheffieldUK
  2. 2.Department of Computer ScienceThe University of HullHullUK

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