| 1. |
- Bohlin, Markus, et al.
(författare)
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A Tool for Gas Turbine Maintenance Scheduling
- 2009. - 20
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Ingår i: Proceedings of the Twenty-First Conference on Innovative Applications of Artificial Intelligence (IAAI'09). - : IEEE Computer Society. - 9781577354239
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- Bohlin, Markus, et al.
(författare)
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Optimization of condition-based maintenance for industrial gas turbines: Requirements and results
- 2009
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Ingår i: Proceedings of the ASME Turbo Expo Volume 5. - 9780791848869 ; , s. 455-464
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
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| 4. |
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| 5. |
- Bohlin, Markus, et al.
(författare)
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Searching for gas turbine maintenance schedules
- 2010
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Ingår i: The AI Magazine. - : Wiley. - 0738-4602. ; 31:1, s. 21-36
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Tidskriftsartikel (refereegranskat)abstract
- Preventive-maintenance schedules occurring in industry are often suboptimal with regard to maintenance coallocation, 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 down time. 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 result's from using integer programming and d'iscuss the deployment of the application. The experimental results indicate that down time reductions up to 65 percent can be achieved, compared to traditional preventive maintenance. In addition, the use of our tool is expected to improve availability by up to 1 percent and to reduce the number of planned maintenance days by 12 percent. Compared to an integer programming approach, our algorithm is not optimal but is much faster and produces results that 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.
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| 6. |
- Doganay, Kivanc
(författare)
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Applications of Optimization Methods in Industrial Maintenance Scheduling and Software Testing
- 2014
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 7. |
- Doganay, Kivanc, et al.
(författare)
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Maintenance plan optimization for a train fleet
- 2010. - 10
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Ingår i: WIT Transactions on the Built Environment Volume 114. - 9781845644680 ; , s. 349-358
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Konferensbidrag (refereegranskat)abstract
- Maintenance planning is an important problem for railways, as well as other application domains that employ machinery with expensive replacements and high downtime costs. In a previous paper, we have developed methods for efficiently finding optimized maintenance schedules for a single unit, and proposed that the maintenance plan should be continuously re-optimized based on the condition of components. However, fleet-level resources, such as the availability of expensive spare parts, have largely been ignored. In this paper, we extend our previous approach by proposing a solution for the fleet level maintenance scheduling problem with spare parts optimization. The new solution is based on a mixed integer linear programming formulation of the problem. We demonstrate the merits of our approach by optimizing instances of maintenance schedules based on maintenance data from railway companies operating in Sweden.
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| 8. |
- Doganay, Kivanc, et al.
(författare)
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Search-Based Testing for Embedded Telecom Software with Complex Input Structures
- 2014
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Ingår i: Testing Software and Systems (Ictss 2014). - Berlin, Heidelberg : Springer. - 9783662448571 ; , s. 205-210
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Konferensbidrag (refereegranskat)abstract
- In this paper, we discuss the application of search-based software testing techniques for unit level testing of a real-world telecommunication middleware at Ericsson. Our current implementation analyzes the existing test cases to handle non-trivial variables such as uninitialized pointers, and to discover any setup code that needs to run before the actual test case, such as setting global system parameters. Hill climbing (HC) and (1+1) evolutionary algorithm (EA) metaheuristic search algorithms are used to generate input data for branch coverage. We compare HC, (1+1) EA, and random search with respect to effectiveness, measured as branch coverage, and efficiency, measured as number of executions needed. Difficulties arising from the specialized execution environment and the adaptations for handling these problems are also discussed.
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| 9. |
- Doganay, Kivanc, et al.
(författare)
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Search-based Testing for Embedded Telecommunication Software with Complex Input Structures: An Industrial Case Study
- 2014. - 6
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we discuss the application of search-based software testing techniques for unit level testing of a real-world telecommunication middleware at Ericsson. Input data for the system under test consists of nested data structures, and includes non-trivial variables such as uninitialized pointers. Our current implementation analyzes the existing test cases to discover how to handle pointers, set global system parameters, and any other setup code that needs to run before the actual test case. Hill climbing (HC) and (1+1) evolutionary algorithm (EA) metaheuristic search algorithms are used to generate input data for branch coverage. We compare HC, (1+1)EA, and random search as a baseline of performance with respect to effectiveness, measured as branch coverage, and efficiency, measured as number of executions needed. Difficulties arising from the specialized execution environment and the adaptations for handling these problems are also discussed.
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| 10. |
- Doganay, Kivanc, et al.
(författare)
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Search Based Testing of Embedded Systems Implemented in IEC 61131-3: An Industrial Case Study
- 2013. - 6
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Ingår i: Proceedings - IEEE 6th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2013. - 9781479913244 ; , s. 425-432
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Konferensbidrag (refereegranskat)abstract
- This paper presents a case study of search-based test generation for embedded system software units developed using the Function Block Diagrams (FBDs), a graphical language in the IEC 61131-3 standard aimed at programmable logic controllers (PLCs). We consider 279 different components from the train control software developed by Bombardier Transportation, a major rail vehicle manufacturer. The software is compiled into C code with a particular structure. We use a modified hill climbing algorithm for generating test data to maximize MC/DC coverage for assignments with logical expressions in the C code, while retaining the semantics of the original FBD implementation. An experimental evaluation for comparing the effectiveness (coverage rate) and the efficiency (required number of executions) of hill climbing algorithm with random testing is presented. The results show that random testing performs well for most units under test, while around 30% of the artifacts significantly benefit from the hill climbing algorithm. Structural properties of the units that affect the performance of hill climbing and random testing are also discussed.
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