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Title: Parameter learning algorithms for continuous model improvement using operational data
Authors: Madsen, Anders L.
Sondberg-Jeppesen, Nicolaj
Jensen, Frank
Sayed, Mohamed S.
Moser, Ulrich
Neto, Luis
Reis, Joao
Lohse, Niels
Keywords: Bayesian networks
Parameter update
Practical application
Issue Date: 2017
Publisher: © Springer
Citation: MADSEN, A.L. ... et al, 2017. Parameter learning algorithms for continuous model improvement using operational data. IN: Antonucci A., Cholvy L., Papini O. (eds). Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017, Lecture Notes in Computer Science, vol 10369, pp.115-124.
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.
Description: This is a pre-copyedited version of a contribution published in [insert title of book and name(s) of Editor(s)] published by Antonucci A., Cholvy L., Papini O. (eds). Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-61581-3_11
Sponsor: 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.
Version: Accepted for publication
DOI: 10.1007/978-3-319-61581-3_11
URI: https://dspace.lboro.ac.uk/2134/25243
Publisher Link: https://doi.org/10.1007/978-3-319-61581-3_11
ISBN: 9783319615813
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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