Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level

Anders L. Madsen; Nicolaj Søndberg-Jeppesen; Mohamed S. Sayed; Michael Peschl; Niels Lohse

doi:10.1007/978-3-319-60045-1_16

Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level

Anders L. Madsen^*, Nicolaj Søndberg-Jeppesen, Mohamed S. Sayed, Michael Peschl, Niels Lohse

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

3 Citations (Scopus)

Abstract

To support health monitoring and life-long capability management for self-sustaining manufacturing systems, next generation machine components are expected to embed sensory capabilities combined with advanced ICT. The combination of sensory capabilities and the use of Object-Oriented Bayesian Networks (OOBNs) supports self-diagnosis at the component level enabling them to become self-aware and support self-healing production systems. This paper describes the use of a modular component-based modelling approach enabled by the use of OOBNs for health monitoring and root-cause analysis of manufacturing systems using a welding controller produced by Harms & Wende (HWH) as an example. The model is integrated into the control software of the welding controller and deployed as a SelComp using the SelSus Architecture for diagnosis and predictive maintenance. The Sel-Comp provides diagnosis and condition monitoring capabilities at the component level while the SelSus Architecture provides these capabilities at a wider system level. The results show significant potential of the solution developed.

Original language	English
Title of host publication	Advances in Artificial Intelligence: From Theory to Practice : 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings
Number of pages	10
Volume	Part II
Publisher	Springer
Publication date	2017
Pages	132-141
ISBN (Print)	978-3-319-60044-4
ISBN (Electronic)	978-3-319-60045-1
DOIs	https://doi.org/10.1007/978-3-319-60045-1_16
Publication status	Published - 2017
Event	30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017 - Arras, France Duration: 27 Jun 2017 → 30 Jun 2017

Conference

Conference	30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017
Country/Territory	France
City	Arras
Period	27/06/2017 → 30/06/2017
Sponsor	International Society of Applied Intelligence (ISAI), The ANR (French National Research Agency) project ASPIQ (ASP Technologies for Querying Large-Scale Multisource Heterogeneous Web Information), University of Artois, France

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10351 LNCS
ISSN	0302-9743

Keywords

OOBNs
Real-world application
Software architecture

Access to Document

10.1007/978-3-319-60045-1_16

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

Madsen, A. L., Søndberg-Jeppesen, N., Sayed, M. S., Peschl, M., & Lohse, N. (2017). Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level. In Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings (Vol. Part II, pp. 132-141). Springer. https://doi.org/10.1007/978-3-319-60045-1_16

Madsen, Anders L. ; Søndberg-Jeppesen, Nicolaj ; Sayed, Mohamed S. et al. / Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level. Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings. Vol. Part II Springer, 2017. pp. 132-141 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10351 LNCS).

@inproceedings{afc20bdd33334eda9161cdd2e034d5a0,

title = "Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level",

abstract = "To support health monitoring and life-long capability management for self-sustaining manufacturing systems, next generation machine components are expected to embed sensory capabilities combined with advanced ICT. The combination of sensory capabilities and the use of Object-Oriented Bayesian Networks (OOBNs) supports self-diagnosis at the component level enabling them to become self-aware and support self-healing production systems. This paper describes the use of a modular component-based modelling approach enabled by the use of OOBNs for health monitoring and root-cause analysis of manufacturing systems using a welding controller produced by Harms & Wende (HWH) as an example. The model is integrated into the control software of the welding controller and deployed as a SelComp using the SelSus Architecture for diagnosis and predictive maintenance. The Sel-Comp provides diagnosis and condition monitoring capabilities at the component level while the SelSus Architecture provides these capabilities at a wider system level. The results show significant potential of the solution developed.",

keywords = "OOBNs, Real-world application, Software architecture",

author = "Madsen, {Anders L.} and Nicolaj S{\o}ndberg-Jeppesen and Sayed, {Mohamed S.} and Michael Peschl and Niels Lohse",

year = "2017",

doi = "10.1007/978-3-319-60045-1_16",

language = "English",

isbn = "978-3-319-60044-4",

volume = "Part II",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "132--141",

booktitle = "Advances in Artificial Intelligence: From Theory to Practice",

address = "Germany",

note = "30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017 ; Conference date: 27-06-2017 Through 30-06-2017",

}

Madsen, AL , Søndberg-Jeppesen, N, Sayed, MS, Peschl, M & Lohse, N 2017, Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level. in Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings. vol. Part II, Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10351 LNCS, pp. 132-141, 30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Arras, France, 27/06/2017. https://doi.org/10.1007/978-3-319-60045-1_16

Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level. / Madsen, Anders L.; Søndberg-Jeppesen, Nicolaj; Sayed, Mohamed S. et al.
Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings. Vol. Part II Springer, 2017. p. 132-141 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10351 LNCS).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level

AU - Madsen, Anders L.

AU - Søndberg-Jeppesen, Nicolaj

AU - Sayed, Mohamed S.

AU - Peschl, Michael

AU - Lohse, Niels

PY - 2017

Y1 - 2017

N2 - To support health monitoring and life-long capability management for self-sustaining manufacturing systems, next generation machine components are expected to embed sensory capabilities combined with advanced ICT. The combination of sensory capabilities and the use of Object-Oriented Bayesian Networks (OOBNs) supports self-diagnosis at the component level enabling them to become self-aware and support self-healing production systems. This paper describes the use of a modular component-based modelling approach enabled by the use of OOBNs for health monitoring and root-cause analysis of manufacturing systems using a welding controller produced by Harms & Wende (HWH) as an example. The model is integrated into the control software of the welding controller and deployed as a SelComp using the SelSus Architecture for diagnosis and predictive maintenance. The Sel-Comp provides diagnosis and condition monitoring capabilities at the component level while the SelSus Architecture provides these capabilities at a wider system level. The results show significant potential of the solution developed.

AB - To support health monitoring and life-long capability management for self-sustaining manufacturing systems, next generation machine components are expected to embed sensory capabilities combined with advanced ICT. The combination of sensory capabilities and the use of Object-Oriented Bayesian Networks (OOBNs) supports self-diagnosis at the component level enabling them to become self-aware and support self-healing production systems. This paper describes the use of a modular component-based modelling approach enabled by the use of OOBNs for health monitoring and root-cause analysis of manufacturing systems using a welding controller produced by Harms & Wende (HWH) as an example. The model is integrated into the control software of the welding controller and deployed as a SelComp using the SelSus Architecture for diagnosis and predictive maintenance. The Sel-Comp provides diagnosis and condition monitoring capabilities at the component level while the SelSus Architecture provides these capabilities at a wider system level. The results show significant potential of the solution developed.

KW - OOBNs

KW - Real-world application

KW - Software architecture

UR - http://www.scopus.com/inward/record.url?scp=85026298908&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-60045-1_16

DO - 10.1007/978-3-319-60045-1_16

M3 - Article in proceeding

AN - SCOPUS:85026298908

SN - 978-3-319-60044-4

VL - Part II

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 132

EP - 141

BT - Advances in Artificial Intelligence: From Theory to Practice

PB - Springer

T2 - 30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017

Y2 - 27 June 2017 through 30 June 2017

ER -

Madsen AL , Søndberg-Jeppesen N, Sayed MS, Peschl M, Lohse N. Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level. In Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings. Vol. Part II. Springer. 2017. p. 132-141. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10351 LNCS). doi: 10.1007/978-3-319-60045-1_16

Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level

Abstract

Conference

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

Cite this