oru.sePublikationer
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Increasing availability of industrial systems through data stream mining
Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.ORCID iD: 0000-0002-2014-1308
2011 (English)In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 60, no 2, 195-205 p.Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

Improving industrial product reliability, maintainability and thus availability is a challenging task for many industrial companies. In industry, there is a growing need to process data in real time, since the generated data volume exceeds the available storage capacity. This paper consists of a review of data stream mining and data stream management systems aimed at improving product availability. Further, a newly developed and validated grid-based classifier method is presented and compared to one-class support vector machine (OCSVM) and a polygon-based classifier.

The results showed that, using 10% of the total data set to train the algorithm, all three methods achieved good (>95% correct) overall classification accuracy. In addition, all three methods can be applied on both offline and online data.

The speed of the resultant function from the OCSVM method was, not surprisingly, higher than the other two methods, but in industrial applications the OCSVMs' comparatively long time needed for training is a possible challenge. The main advantage of the grid-based classification method is that it allows for calculation of the probability (%) that a data point belongs to a specific class, and the method can be easily modified to be incremental.

The high classification accuracy can be utilized to detect the failures at an early stage, thereby increasing the reliability and thus the availability of the product (since availability is a function of maintainability and reliability). In addition, the consequences of equipment failures in terms of time and cost can be mitigated.

Place, publisher, year, edition, pages
Elsevier, 2011. Vol. 60, no 2, 195-205 p.
Keyword [en]
Availability; Data stream mining; Data stream management system; Industrial systems; Grid-based classifier
National Category
Computer Science Mechanical Engineering
Research subject
Computer Science; Mechanical Engineering
Identifiers
URN: urn:nbn:se:oru:diva-50813DOI: 10.1016/j.cie.2010.10.008ISI: 000287290100002Scopus ID: 2-s2.0-78951484172OAI: oai:DiVA.org:oru-50813DiVA: diva2:936987
Note

Funding Agency:

SSPI (SSF)

Available from: 2016-06-14 Created: 2016-06-14 Last updated: 2017-11-28Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Löfstrand, Magnus

Search in DiVA

By author/editor
Löfstrand, Magnus
In the same journal
Computers & industrial engineering
Computer ScienceMechanical Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 202 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf