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Applying Object-Oriented Bayesian Networks for smart diagnosis and health monitoring at both component and factory level

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conference contribution
posted on 22.03.2017, 11:07 by Anders L. Madsen, Nicolaj Sondberg-Jeppesen, Mohamed S. Sayed, Michael Peschl, Niels Lohse
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 SelComp 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.

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

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

Citation

MADSEN, A.L. ... et al, 2017. Applying Object-Oriented Bayesian Networks for smart diagnosis and health monitoring at both component and factory level. IN: Benferhat, S. Tabia, K. and Ali, M. (eds.) Advances in Artificial Intelligence: From Theory to Practice: EA/AIE 2017 - The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems, Arras, France, 27th-30th June 2017, Proceedings, Part II. Chaim: Springer, pp. 132-141.

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

20/02/2017

Publication date

2017

Notes

This is a pre-copyedited version of a contribution published in Benferhat, S. Tabia, K. and Ali, M. (eds.) Advances in Artificial Intelligence: From Theory to Practice: EA/AIE 2017 - The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems, Arras, France, 27th-30th June 2017, Proceedings, Part II published by Springer. The definitive authenticated version is available online via https://link.springer.com/book/10.1007/978-3-319-60045-1

Book series

Lecture Notes in Computer Science , 10351

Language

en

Location

Arras, France

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