Embedded intelligence supporting predictive asset management in the energy sector

E. Miguela[ntilde]ez-Martin, David Flynn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years and across a myriad of industries, there has been a realisation that in order to optimise the Remaining Useful Life (RUL) of assets and to maintain optimal system level performance whilst assets age and at times with growing and dynamic loading demands, a transition to predictive maintenance from reactive and traditional condition based monitoring and maintenance is required to achieve return of investment (ROI) and performance targets. A sector driven by security and a need to defer investment within the asset base is the Energy sector. After a brief introduction to maintenance process's in the oil and gas domain, this paper presents a novel approach to hierarchical predictive maintenance of assets in through a distributed architecture, represented as domain knowledge-based system, that provides a viable solution for systems containing similar multiple asset
Original languageEnglish
Title of host publicationProceedings of the Asset Management Conference 2015
Pages7-14
Number of pages8
DOIs
Publication statusPublished - Nov 2015
EventAsset Management Conference 2015 - IET Savoy, London, United Kingdom
Duration: 25 Nov 201526 Nov 2015
Conference number: CP669
http://digital-library.theiet.org/content/conferences/cp669;jsessionid=1qid4i64z14oy.x-iet-live-01

Conference

ConferenceAsset Management Conference 2015
CountryUnited Kingdom
CityLondon
Period25/11/1526/11/15
Internet address

Keywords

  • Asset management
  • Condition monitoring
  • embedded intelligence
  • Engineering
  • Knowledge based systems

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  • Cite this

    Miguela[ntilde]ez-Martin, E., & Flynn, D. (2015). Embedded intelligence supporting predictive asset management in the energy sector. In Proceedings of the Asset Management Conference 2015 (pp. 7-14) https://doi.org/10.1049/cp.2015.1752