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

*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

2 Citationer (Scopus)
178 Downloads (Pure)

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 online learning from operational data using each of the four algorithms.

OriginalsprogEngelsk
TitelSymbolic and Quantitative Approaches to Reasoning with Uncertainty : 14th European Conference, ECSQARU 2017, Proceedings
Antal sider10
ForlagSpringer
Publikationsdato2017
Sider115-124
ISBN (Trykt)978-3-319-61580-6
ISBN (Elektronisk)978-3-319-61581-3
DOI
StatusUdgivet - 2017
Begivenhed14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017 - Lugano, Schweiz
Varighed: 10 jul. 201714 jul. 2017

Konference

Konference14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017
Land/OmrådeSchweiz
ByLugano
Periode10/07/201714/07/2017
NavnLecture Notes In Artificial Intelligence
Nummer10369
ISSN0302-9743

Fingeraftryk

Dyk ned i forskningsemnerne om 'Parameter learning algorithms for continuous model improvement using operational data'. Sammen danner de et unikt fingeraftryk.

Citationsformater