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Optimization of condition-based maintenance for industrial gas turbines: Requirements and results
SICS.ORCID iD: 0000-0003-1597-6738
Siemens Industrial Turbomachinery AB.
SICS.
Siemens Industrial Turbomachinery AB.
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2009 (English)In: Proceedings of the ASME Turbo Expo Volume 5, 2009, p. 455-464Conference paper, Published paper (Refereed)
Abstract [en]

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-madegas 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 preventivemaintenance 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, down-time reductions of approximately 12 % are expected, due solely to improved planning. This indicates significant improvements.

Place, publisher, year, edition, pages
2009. p. 455-464
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-7379ISBN: 9780791848869 (print)OAI: oai:DiVA.org:mdh-7379DiVA, id: diva2:272137
Conference
2009 ASME Turbo Expo; Orlando, FL; United States; 8 June 2009 through 12 June 2009
Available from: 2009-10-14 Created: 2009-10-14 Last updated: 2013-12-03Bibliographically approved
In thesis
1. A Study of Combinatorial Optimization Problems in Industrial Computer Systems
Open this publication in new window or tab >>A Study of Combinatorial Optimization Problems in Industrial Computer Systems
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A combinatorial optimization problem is an optimization problem where the number of possible solutions are finite and grow combinatorially with the problem size. Combinatorial problems exist everywhere in industrial systems. This thesis focuses on solving three such problems which arise within two different areas where industrial computer systems are often used. Within embedded systems and real-time systems, we investigate the problems of allocating stack memory for an system where a shared stacks may be used, and of estimating the highest response time of a task in a system of industrial complexity. We propose a number of different algorithms to compute safe upper bounds on run-time stack usage whenever the system supports stack sharing. The algorithms have in common that they can exploit commonly-available information regarding timing behaviour of the tasks in the system. Given upper bounds on the individual stack usage of the tasks, it is possible to estimate the worst-case stack behaviour by analysing the possible and impossible preemption patterns. Using relations on offset and precedences, we form a preemption graph, which is further analysed to find safe upper-bounds on the maximal preemptions chain in the system. For the special case where all tasks exist in a single static schedule and share a single stack, we propose a polynomial algorithm to solve the problem. For generalizations of this problem, we propose an exact branch-and-bound algorithm for smaller problems and a polynomial heuristic algorithm for cases where the branch-and-bound algorithm fails to find a solution in reasonable time. All algorithms are evaluated in comprehensive experimental studies. The polynomial algorithm is implemented and shipped in the developer tool set for a commercial real-time operating system, Rubus OS. The second problem we study in the thesis is how to estimate the highest response time of a specified task in a complex industrial real-time system. The response-time analysis is done using a best-effort approach, where a detailed model of the system is simulated on input constructed using a local search procedure. In an evaluation on three different systems we can see that the new algorithm were able to produce higher response times much faster than what has previously been possible. Since the analysis is based on simulation and measurement, the results are not safe in the sense that they are always higher or equal to the true response time of the system. The value of the method lies instead in that it makes it possible to analyse complex industrial systems which cannot be analysed accurately using existing safe approaches. The third problem is in the area of maintenance planning, and focus on how to dynamically plan maintenance for industrial systems. Within this area we have focused on industrial gas turbines and rail vehicles.  We have developed algorithms and a planning tool which can be used to plan maintenance for gas turbines and other stationary machinery. In such problems, it is often the case that performing several maintenance actions at the same time is beneficial, since many of these jobs can be done in parallel, which reduces the total downtime of the unit. The core of the problem is therefore how to (or how not to) group maintenance activities so that a composite cost due to spare parts, labor and loss of production due to downtime is minimized. We allow each machine to have individual schedules for each component in the system. For rail vehicles, we have evaluated the effect of replanning maintenance in the case where the component maintenance deadline is set to reflect a maximum risk of breakdown in a Gaussian failure distribution. In such a model, we show by simulation that replanning of maintenance can reduce the number of maintenance stops when the variance and expected value of the distribution are increased.  For the gas turbine maintenance planning problem, we have evaluated the planning software on a real-world scenario from the oil and gas industry and compared it to the solutions obtained from a commercial integer programming solver. It is estimated that the availability increase from using our planning software is between 0.5 to 1.0 %, which is substantial considering that availability is currently already at 97-98 %.

Place, publisher, year, edition, pages
Västerås: Mälardalen University Press, 2009
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 79
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-7381 (URN)978-91-86135-47-8 (ISBN)
Public defence
2009-12-14, Beta, Högskoleplan 1, Västerås, 14:00 (English)
Opponent
Supervisors
Available from: 2009-10-29 Created: 2009-10-14 Last updated: 2018-01-12Bibliographically approved

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Bohlin, MarkusDoganay, Kivanc

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