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Optimization of Condition-Based Maintenance for Industrial Gas Turbines: Requirements and Results

[+] Author Affiliations
Markus Bohlin, Anders Holst, Kivanc Doganay

Swedish Institute of Computer Science, Kista, Sweden

Mathias Wärja, Pontus Slottner

Siemens Industrial Turbomachinery AB, Finspong, Sweden

Paper No. GT2009-59935, pp. 455-464; 10 pages
  • ASME Turbo Expo 2009: Power for Land, Sea, and Air
  • Volume 5: Microturbines and Small Turbomachinery; Oil and Gas Applications
  • Orlando, Florida, USA, June 8–12, 2009
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4886-9 | eISBN: 978-0-7918-3849-5
  • Copyright © 2009 by ASME


In oil and gas applications, the careful planning and execution of preventive maintenance is important due to the high costs associated with shutdown of critical equipment. Optimization and lifetime management for equipment such as gas turbines is therefore crucial in order to achieve high availability and reliability. In this paper, a novel condition-based gas turbine maintenance strategy is described and evaluated. Using custom-made gas turbine maintenance planning software, maintenance is repeatedly reoptimized to fit into the time intervals where production losses are least costly and result in the lowest possible impact. The strategy focuses on accurate online lifetime estimates for gas turbine components, where algorithms predicting future maintenance requirements are used to produce maintenance deadlines. This ensures that the gas turbines are maintained in accordance with the conditions on site. To show the feasibility and economic effects of a customer-adapted maintenance planning process, the maintenance plan for a gas turbine used in a real-world scenario is optimized using a combinatorial optimization algorithm and input from gas turbine operation data, maintenance schedules and operator requirements. The approach was validated through the inspection of a reference gas turbine after a predetermined time interval. It is shown that savings may be substantial compared to a traditional preventive maintenance plan. In the evaluation, typical cost reductions range from 25 to 65%. The calculated availability increase in practice is estimated to range from 0.5 to 1%. In addition, downtime reductions of approximately 12% are expected, due solely to improved planning. This indicates significant improvements.

Copyright © 2009 by ASME



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