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Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application
Department of Information Technology, Division of Computing Science, Uppsala, Sweden.
Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
Department of Information Technology, Division of Computing Science, Uppsala, Sweden.ORCID iD: 0000-0002-2014-1308
Bosch Rexroth Mellansel AB, Mellansel, Sweden.
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2014 (English)In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 65, no 8, 1126-1135 p.Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods.

In this work, we investigated the ability and the performance of applying two fault detection methods to query data streams produced from hydraulic drive systems. A knowledge-based method was compared to a data-driven method. A fault detection system based on a data stream management system (DSMS) was developed in order to test and compare the two methods using data from real hydraulic drive systems.

The knowledge-based method was based on causal models (fault trees), and principal component analysis (PCA) was used to build the data-driven model. The performance of the methods in terms of accuracy and speed, was examined using normal and physically simulated fault data. The results show that both methods generate queries fast enough to query the data streams online, with a similar level of fault detection accuracy. The industrial applications of both methods include monitoring of individual industrial mechanical systems as well as fleets of such systems. One can conclude that both methods may be used to increase industrial system availability.

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 65, no 8, 1126-1135 p.
Keyword [en]
Fault detection; Data-driven; Knowledge-based; Data stream mining; Data stream management system; Product development
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-50810DOI: 10.1016/j.compind.2014.06.003ISI: 000342326300003Scopus ID: 2-s2.0-84929076117OAI: oai:DiVA.org:oru-50810DiVA: diva2:936985
Funder
VINNOVA, 2012-00705
Note

Funding Agency:

SSPI project (Scalable search of product lifecycle information) - Swedish Foundation for Strategic Research SSF: RIT08-0041

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

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