Searching for Gas Turbine Maintenance Schedules

  • Markus Bohlin Swedish Institute of Computer Science
  • Kivanc Doganay Swedish Institute of Computer Science
  • Per Kreuger Swedish Institute of Computer Science
  • Rebecca Steinert Swedish Institute of Computer Science
  • Mathias Warja Siemens Industrial Turbomachinery AB
Keywords: Maintenence plannning, optimisation


Preventive maintenance schedules occurring in industry are often suboptimal with regard to maintenance coal-location, 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 downtime. 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 results from using integer 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, the use of our tool is expected to improve availability with up to 1% and reduce the number of planned maintenance days by 12%. Compared to a integer programming approach, our algorithm is not optimal, but is much 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.

Author Biographies

Markus Bohlin, Swedish Institute of Computer Science

Industrial Applications and Methods Lab

Kivanc Doganay, Swedish Institute of Computer Science
Per Kreuger, Swedish Institute of Computer Science
Scientific LeaderIndustrial Applications and Methods Laboratory
Rebecca Steinert, Swedish Institute of Computer Science
Mathias Warja, Siemens Industrial Turbomachinery AB