SwePub
Sök i LIBRIS databas

  Utökad sökning

onr:"swepub:oai:DiVA.org:ri-23632"
 

Sökning: onr:"swepub:oai:DiVA.org:ri-23632" > A Tool for Gas Turb...

  • Bohlin, MarkusRISE,SICS,Swedish Institute of Computer Science, SICS (författare)

A Tool for Gas Turbine Maintenance Scheduling

  • 20
  • Artikel/kapitelEngelska2009

Förlag, utgivningsår, omfång ...

  • IEEE Computer Society,2009
  • electronicrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:ri-23632
  • https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-23632URI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-7374URI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:kon swepub-publicationtype

Anmärkningar

  • Proceedings of the Twenty-First Conference on Innovative Applications of Artificial Intelligence (IAAI'09) published by IEEE Computer Society
  • We describe the implementation and deployment of a software decision support tool for the maintenance planning of gas turbines. The tool is used to plan the maintenance for turbines manufactured and maintained by Siemens Industrial Turbomachinery AB (SIT AB) with the goal to reduce the direct maintenance costs and the often very costly production losses during maintenance downtime. The optimization problem is formally defined, and we argue that feasibility in it is NP-complete. We outline a heuristic algorithm that can quickly solve the problem for practical purposes, and validate the approach on a real-world scenario based on an oil production facility. We also compare the performance of our algorithm with results from using mixed integer linear programming, and discuss the deployment of the application. The experimental results indicate that downtime reductions up to 65% can be achieved, compared to traditional preventive maintenance. In addition, using our tool is expected to improve availability with up to 1% and reduce the number of planned maintenance days with 12%. Compared to a mixed integer programming approach, our algorithm not optimal, but is orders of magnitude faster and produces results which are useful in practice. Our test results and SIT AB’s estimates based on operational use both indicate that significant savings can be achieved by using our software tool, compared to maintenance plans with fixed intervals.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Doganay, KivancRISE,SICS,Swedish Institute of Computer Science, SICS(Swepub:mdh)kdy02 (författare)
  • Kreuger, PerRISE,Decisions, Networks and Analytics lab,Swedish Institute of Computer Science, SICS(Swepub:ri)per.kreuger@ri.se (författare)
  • Steinert, RebeccaRISE,Decisions, Networks and Analytics lab,Swedish Institute of Computer Science, SICS(Swepub:ri)rebecca.steinert@ri.se (författare)
  • Wärja, MathiasSiemens Industrial Turbomachinery AB (författare)
  • RISESICS (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Proceedings of the Twenty-First Conference on Innovative Applications of Artificial Intelligence (IAAI'09): IEEE Computer Society9781577354239

Internetlänk

Hitta via bibliotek

Till lärosätets databas

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy