A Tool for Gas Turbine Maintenance Scheduling
Number of Authors: 5
2009 (English)In: Proceedings of the Twenty-First Conference on Innovative Applications of Artificial Intelligence (IAAI'09), IEEE Computer Society , 2009, 20Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE Computer Society , 2009, 20.
Computer and Information Science
IdentifiersURN: urn:nbn:se:ri:diva-23632OAI: oai:DiVA.org:ri-23632DiVA: diva2:1042708
Twenty-First Conference on Innovative Applications of Artificial Intelligence (IAAI'09)
Proceedings of the Twenty-First Conference on Innovative Applications of Artificial Intelligence (IAAI'09) published by IEEE Computer Society2016-10-312016-10-31