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Searching for gas turbine maintenance schedules
KTH, School of Computer Science and Communication (CSC).
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2010 (English)In: The AI Magazine, ISSN 0738-4602, Vol. 31, no 1, 21-36 p.Article in journal (Refereed) Published
Abstract [en]

Preventive-maintenance schedules occurring in industry are often suboptimal with regard to maintenance coallocation, loss-of-production costs, and availability. We describe the implementation and deployment of a software decision support, tool for the maintenance planning of gas turbines, with the goal of reducing the direct maintenance costs and the often costly production losses during maintenance down time. The optimization problem is formally defined, and we argue that the feasibility version 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 result's from using integer programming and d'iscuss the deployment of the application. The experimental results indicate that down time reductions up to 65 percent can be achieved, compared to traditional preventive maintenance. In addition, the use of our tool is expected to improve availability by up to 1 percent and to reduce the number of planned maintenance days by 12 percent. Compared to an integer programming approach, our algorithm is not optimal but is much faster and produces results that 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.

Place, publisher, year, edition, pages
2010. Vol. 31, no 1, 21-36 p.
Keyword [en]
Co-allocation, Decision supports, Direct maintenance costs, Down time, Maintenance down time, Maintenance planning, Maintenance plans, Maintenance schedules, NP Complete, Oil production, Operational use, Optimization problems, Planned maintenance, Production cost, Production loss, Real-world scenario, Software tool, Test results, Turbine maintenance
National Category
Computer Science
URN: urn:nbn:se:kth:diva-150049ScopusID: 2-s2.0-79951935661OAI: diva2:745265

QC 20140910

Available from: 2014-09-10 Created: 2014-08-29 Last updated: 2014-09-10Bibliographically approved

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Steinert, Rebecca
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