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Parameter learning algorithms for continuous model improvement using operational data

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conference contribution
posted on 02.06.2017 by Anders L. Madsen, Nicolaj Sondberg-Jeppesen, Frank Jensen, Mohamed S. Sayed, Ulrich Moser, Luis Neto, Joao Reis, Niels Lohse
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.

Funding

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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Fourteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2017)

Citation

MADSEN, A.L. ... et al, 2017. Parameter learning algorithms for continuous model improvement using operational data. IN: Antonucci A., Cholvy L. and Papini O. (eds). Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. Lecture Notes in Computer Science, vol 10369. Cham: Springer, pp.115-124.

Publisher

© Springer

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

14/04/2017

Publication date

2017

Notes

This is a pre-copyedited version of a contribution published in Antonucci A., Cholvy L. and Papini O. (eds). Symbolic and Quantitative Approaches to Reasoning with Uncertainty published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-61581-3_11. This paper was also presented at the 14th European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU 2017), Lugano, Switzerland, 10th-14th July 2017.

ISBN

9783319615813;9783319615806

Book series

Lecture Notes in Computer Science; 10369

Language

en

Location

Lugano, Switzerland

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