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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
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
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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
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
2014. Vol. 65, no 8, 1126-1135 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-232173DOI: 10.1016/j.compind.2014.06.003OAI: oai:DiVA.org:uu-232173DiVA: diva2:746803
Funder
Swedish Foundation for Strategic Research , RIT08-0041
Available from: 2014-07-05 Created: 2014-09-15 Last updated: 2017-12-05Bibliographically approved

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Alzghoul, AhmadLöfstrand, Magnus

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