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An Integrated Adaptive Maintenance Concept
SICS.
SICS.ORCID iD: 0000-0003-1597-6738
SICS.ORCID iD: 0000-0003-2234-1255
SICS.
2010 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a novel maintenance concept based on condition monitoring and dynamic maintenance packaging, by showing how to connect the information flow from low-level sensors to high-level operations and planning under uncertainty. Today, condition-based maintenance systems are focused on data collection and custom-made rule based systems for data analysis. In many cases, the focus is on measuring "everything" without considering how to use the measurements. In addition, the measurements are often noisy and the future is unpredictable which adds a lot of uncertainty. As a consequence, maintenance is often planned in advance and not replanned when new condition data is available. This often reduces the benefits of condition monitoring. The concept is based on the combination of robust, dynamically adapted maintenance optimization and statistical data analysis where the uncertainty is considered. This approach ties together low-level data acquisition and high-level planning and optimization. The concept has been illustrated in a context of rail vehicle maintenance, where measurements of brake pad and pantograph contact strip wear is used to predict the near future condition, and plan the maintenance activities.

Place, publisher, year, edition, pages
Tokyo, Japan, 2010.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-21313OAI: oai:DiVA.org:mdh-21313DiVA: diva2:650373
Conference
International Conference on Condition Monitoring and Diagnosis 2010, 6-11 September 2010, Tokyo, Japan.
Available from: 2013-09-20 Created: 2013-09-11 Last updated: 2014-09-15Bibliographically approved
In thesis
1. 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 Science
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: 2014-10-08Bibliographically approved

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Citation style
  • apa
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Output format
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