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)


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.


Bayesian networks Parameter update Practical application 



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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