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

doi:10.1007/978-3-319-61581-3_11

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 proceeding › Konferenceartikel i proceeding › Forskning › peer review

2 Citationer (Scopus)

181 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.

Originalsprog	Engelsk
Titel	Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 14th European Conference, ECSQARU 2017, Proceedings
Antal sider	10
Forlag	Springer
Publikationsdato	2017
Sider	115-124
ISBN (Trykt)	978-3-319-61580-6
ISBN (Elektronisk)	978-3-319-61581-3
DOI	https://doi.org/10.1007/978-3-319-61581-3_11
Status	Udgivet - 2017
Begivenhed	14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017 - Lugano, Schweiz Varighed: 10 jul. 2017 → 14 jul. 2017

Konference

Konference	14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017
Land/Område	Schweiz
By	Lugano
Periode	10/07/2017 → 14/07/2017

Navn	Lecture Notes In Artificial Intelligence
Nummer	10369
ISSN	0302-9743

Adgang til dokumentet

10.1007/978-3-319-61581-3_11

Parameter_Learning_Algorithms_for_Continuous_Model_Improvement_Using_Operational_DataAccepteret manuskript, 462 KBLicens: CC BY-NC-ND 4.0

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

Andre filer og links

http://www.scopus.com/inward/record.url?scp=85025175994&partnerID=8YFLogxK

Citationsformater

Madsen, A. L., Jeppesen, N. S., Jensen, F., Sayed, M. S., Moser, U., Neto, L., Reis, J., & Lohse, N. (2017). Parameter learning algorithms for continuous model improvement using operational data. I Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Proceedings (s. 115-124). Springer. https://doi.org/10.1007/978-3-319-61581-3_11

@inproceedings{81f481f6737c4cb786737dcbdd33df18,

title = "Parameter learning algorithms for continuous model improvement using operational data",

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.",

keywords = "Bayesian networks, Parameter update, Practical application",

author = "Madsen, {Anders L.} and Jeppesen, {Nicolaj S{\o}ndberg} and Frank Jensen and Sayed, {Mohamed S.} and Ulrich Moser and Luis Neto and Joao Reis and Niels Lohse",

year = "2017",

doi = "10.1007/978-3-319-61581-3_11",

language = "English",

isbn = "978-3-319-61580-6",

series = "Lecture Notes In Artificial Intelligence",

publisher = "Springer",

number = "10369",

pages = "115--124",

booktitle = "Symbolic and Quantitative Approaches to Reasoning with Uncertainty",

address = "Germany",

note = "14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017 ; Conference date: 10-07-2017 Through 14-07-2017",

}

Madsen, AL , Jeppesen, NS, Jensen, F, Sayed, MS, Moser, U, Neto, L, Reis, J & Lohse, N 2017, Parameter learning algorithms for continuous model improvement using operational data. i Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Proceedings. Springer, Lecture Notes In Artificial Intelligence, nr. 10369, s. 115-124, 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017, Lugano, Schweiz, 10/07/2017. https://doi.org/10.1007/978-3-319-61581-3_11

Parameter learning algorithms for continuous model improvement using operational data. / Madsen, Anders L.; Jeppesen, Nicolaj Søndberg; Jensen, Frank et al.
Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Proceedings. Springer, 2017. s. 115-124 (Lecture Notes In Artificial Intelligence; Nr. 10369).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Parameter learning algorithms for continuous model improvement using operational data

AU - Madsen, Anders L.

AU - Jeppesen, Nicolaj Søndberg

AU - Jensen, Frank

AU - Sayed, Mohamed S.

AU - Moser, Ulrich

AU - Neto, Luis

AU - Reis, Joao

AU - Lohse, Niels

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Bayesian networks

KW - Parameter update

KW - Practical application

UR - http://www.scopus.com/inward/record.url?scp=85025175994&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-61581-3_11

DO - 10.1007/978-3-319-61581-3_11

M3 - Article in proceeding

AN - SCOPUS:85025175994

SN - 978-3-319-61580-6

T3 - Lecture Notes In Artificial Intelligence

SP - 115

EP - 124

BT - Symbolic and Quantitative Approaches to Reasoning with Uncertainty

PB - Springer

T2 - 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017

Y2 - 10 July 2017 through 14 July 2017

ER -

Madsen AL , Jeppesen NS, Jensen F, Sayed MS, Moser U, Neto L et al. Parameter learning algorithms for continuous model improvement using operational data. I Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Proceedings. Springer. 2017. s. 115-124. (Lecture Notes In Artificial Intelligence; Nr. 10369). doi: 10.1007/978-3-319-61581-3_11

Parameter learning algorithms for continuous model improvement using operational data

Abstract

Konference

Adgang til dokumentet

AUB Link

Andre filer og links

Fingeraftryk

Citationsformater