Parameter Learning Algorithms for Continuous Model Improvement Using Operational Data

  • Anders L. Madsen
  • Nicolaj Søndberg Jeppesen
  • Frank Jensen
  • Mohamed S. Sayed
  • Ulrich Moser
  • Luis Neto
  • Joao Reis
  • Niels Lohse
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)

Abstract

In this paper, we consider the application of object-oriented Bayesian networks to failure diagnostics in manufacturing systems and continuous model improvement based on operational data. The analysis is based on an object-oriented Bayesian network developed for failure diagnostics of a one-dimensional pick-and-place industrial robot developed by IEF-Werner GmbH. We consider four learning algorithms (batch Expectation-Maximization (EM), incremental EM, Online EM and fractional updating) for parameter updating in the object-oriented Bayesian network using a real operational dataset. Also, we evaluate the performance of the considered algorithms on a dataset generated from the model to determine which algorithm is best suited for recovering the underlying generating distribution. The object-oriented Bayesian network has been integrated into both the control software of the robot as well as into a software architecture that supports diagnostic and prognostic capabilities of devices in manufacturing systems. We evaluate the time performance of the architecture to determine the feasibility of on-line learning from operational data using each of the four algorithms.

Keywords

Bayesian networks Parameter update Practical application 

Notes

Acknowledgments

This work is part of the project “Health Monitoring and Life-Long Capability Management for SELf-SUStaining Manufacturing Systems (SelSus)” which is funded by the Commission of the European Communities under the 7th Framework Programme, Grant agreement no: 609382. We would like to thank Andres Masegosa for discussions on the Online EM algorithm and the reviewers for their insightful comments, which have helped to improve the paper.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anders L. Madsen
    • 1
    • 2
  • Nicolaj Søndberg Jeppesen
    • 1
  • Frank Jensen
    • 1
  • Mohamed S. Sayed
    • 3
  • Ulrich Moser
    • 4
  • Luis Neto
    • 5
  • Joao Reis
    • 5
  • Niels Lohse
    • 3
  1. 1.HUGIN EXPERT A/SAalborgDenmark
  2. 2.Department of Computer ScienceAalborg UniversityAalborgDenmark
  3. 3.Loughborough UniversityLoughboroughUK
  4. 4.IEF-Werner GmbHFurtwangenGermany
  5. 5.Instituto de Sistemas e. Robotica AssociacaoPortoPortugal

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