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
Data stream forecasting for system fault prediction
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
Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
2012 (English)In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 62, no 4, 972-978 p.Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

Competition among today’s industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing. In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM). The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.

Place, publisher, year, edition, pages
Elsevier, 2012. Vol. 62, no 4, 972-978 p.
Keyword [en]
Availability; Data stream management system; Data stream mining; Data stream prediction; Fault detection forecasting; Fault detection system
National Category
Computer Science Mechanical Engineering
Research subject
Computer Science; Mechanical Engineering
Identifiers
URN: urn:nbn:se:oru:diva-50811DOI: 10.1016/j.cie.2011.12.023ISI: 000303092500013Scopus ID: 2-s2.0-84858701770OAI: oai:DiVA.org:oru-50811DiVA: diva2:936991
Note

Funding Agency:

Swedish Foundation for Strategic Research (SSF) through the SSPI

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: 199 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