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A Tool for Gas Turbine Maintenance Scheduling
Swedish Institute of Computer Science, SICS.ORCID iD: 0000-0003-1597-6738
Swedish Institute of Computer Science, SICS.ORCID iD: 0000-0003-2234-1255
Swedish Institute of Computer Science, SICS.
Swedish Institute of Computer Science, SICS.
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2009 (English)In: Proceedings of the 21st Innovative Applications of Artificial Intelligence Conference, IAAI-09, 2009, p. 9-16Conference paper, Published paper (Refereed)
Abstract [en]

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
2009. p. 9-16
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-7374ISBN: 9781577354239 (print)OAI: oai:DiVA.org:mdh-7374DiVA, id: diva2:272122
Conference
21st Innovative Applications of Artificial Intelligence Conference, IAAI-09; Pasadena, CA; United States; 14 July 2009 through 16 July 2009
Available from: 2009-10-14 Created: 2009-10-14 Last updated: 2014-09-15Bibliographically 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
2. Applications of Optimization Methods in Industrial Maintenance Scheduling and Software Testing
Open this publication in new window or tab >>Applications of Optimization Methods in Industrial Maintenance Scheduling and Software Testing
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

As the world is getting more and more competitive, efficiency has become a bigger concern than ever for many businesses. Certain efficiency concerns can naturally be expressed as optimization problems, which is a well studied field in the academia. However, optimization algorithms are not as widely employed in industrial practice as they could. There are various reasons for the lack of widespread adoption. For example, it can be difficult or even impossible for non-experts to formulate a detailed mathematical model of the problem. On the other hand, a scientist usually does not have a deep enough understanding of critical business details, and may fail to capture enough details of the real- world phenomenon of concern. While a model at an arbitrary abstraction level is often good enough to demonstrate the optimization approach, ignoring relevant aspects can easily render the solution impractical for the industry. This is an important problem, because applicability concerns hinder the possible gains that can be achieved by using the academic knowledge in industrial practice. In this thesis, we study the challenges of industrial optimization problems in the form of four case studies at four different companies, in the domains of maintenance schedule optimization and search-based software testing. Working with multiple case studies in different domains allows us to better understand the possible gains and practical challenges in applying optimization methods in an industrial setting. Often there is a need to trade precision for applicability, which is typically very context dependent. Therefore, we compare our results against base values, e.g., results from simpler algorithms or the state of the practice in the given context, where applicable. Even though we cannot claim that optimization methods are applicable in all situations, our work serves as an empirical evidence for the usability of optimization methods for improvements in different industrial contexts. We hope that our work can encourage the adoption of optimization techniques by more industrial practitioners.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2014
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 180
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-25944 (URN)978-91-7485-163-2 (ISBN)
Presentation
2014-10-14, R3-131, Mälardalens högskola, Västerås, 13:30 (English)
Opponent
Supervisors
Available from: 2014-09-15 Created: 2014-09-14 Last updated: 2018-01-11Bibliographically approved

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Bohlin, MarkusDoganay, Kivanc

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