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Addressing concept drift to improve system availability by updating one-class data-driven models
Department of Information Technology, Division of Computing Science, Uppsala, Sweden.
Department of Information Technology, Division of Computing Science, Uppsala, Sweden.ORCID iD: 0000-0002-2014-1308
2015 (English)In: Evolving Systems, ISSN 1868-6478, E-ISSN 1868-6486, Vol. 6, no 3, 187-198 p.Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

Data-driven models have been used to detect system faults, thereby increasing industrial system availability. The ability to search data streams while dealing with concept drift are challenges for data-driven models. The objective of this work is to demonstrate a general method to manage concept drift when using one-class data-driven models. The method has been used to develop an automatically retrained and updated polygon-based model. In this paper, the available industrial data allowed for use of one-class data-driven models, and the polygon-based model was selected because it has previously been successful. Possible scenarios that allow one-class data-driven models to be retrained or updated were identified. Based on the identified scenarios, a method to automatically update a polygon-based model online is proposed. The method has been tested and verified using data collected from a Bosch Rexroth Mellansel AB hydraulic drive system. Data representing relevant faults was inserted into the data set in close collaboration with engineers from the company. The results show that the developed polygon-based model method was able to address the concept drift issue and was able to significantly improve the classification accuracy compared to the static polygon-based model. Thereby, the model could significantly improve industrial system availability when applied in the relevant production process. This paper shows that the developed polygon-based model requires small memory space while its updating procedure is simple and fast. Finally, the identified scenarios may be helpful as input for supporting other one-class data-driven models to cope with concept drift, thus increasing the generalizability of the results.

Place, publisher, year, edition, pages
Springer, 2015. Vol. 6, no 3, 187-198 p.
Keyword [en]
Availability; Concept drift; Data stream mining; Industry; Polygon-based method
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-50809DOI: 10.1007/s12530-014-9107-zScopus ID: 2-s2.0-84939606471OAI: oai:DiVA.org:oru-50809DiVA: diva2:936996
Funder
EU, FP7, Seventh Framework Programme
Available from: 2016-06-14 Created: 2016-06-14 Last updated: 2017-10-17Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
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More languages
Output format
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