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Scalable validation of industrial equipment using a functional DSMS
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)ORCID iD: 0000-0002-0455-2166
Volvo Construction Equipment, Eskilstuna, Sweden.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
ProcessIT Innovations, Luleå University of Technology, Luleå, Sweden.
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2017 (English)In: Journal of Intelligent Information Systems, ISSN 0925-9902, E-ISSN 1573-7675, Vol. 48, no 3, 553-577 p.Article in journal (Refereed) Published
Abstract [en]

A stream validation system called SVALI is developed in order to continuously validate correct behavior of industrial equipment. A functional data model allows the user to define meta-data, analyses, and queries about the monitored equipment in terms of types and functions. Two different approaches to validate that sensor readings in a data stream indicate correct equipment behavior are supported: with the model-and-validate approach anomalies are detected based on a physical model, while with learn-and-validate anomalies are detected by comparing streaming data with a model of normal behavior learnt during a training period. Both models are expressed on a high level using the functional data model and query language. The experiments show that parallel stream processing enables SVALI to scale very well with respect to system throughput and response time. The paper is based on a real world application for wheel loader slippage detection at Volvo Construction Equipment implemented in SVALI.

Place, publisher, year, edition, pages
2017. Vol. 48, no 3, 553-577 p.
Keyword [en]
Data stream management, Distributed stream systems, Data stream validation, Parallelization, Anomaly detection
National Category
Computer Science
Research subject
Computer Science with specialization in Database Technology
Identifiers
URN: urn:nbn:se:uu:diva-292767DOI: 10.1007/s10844-016-0427-2ISI: 000401468300004OAI: oai:DiVA.org:uu-292767DiVA: diva2:926611
Funder
EU, FP7, Seventh Framework ProgrammeSwedish Foundation for Strategic Research , RIT08-0041
Available from: 2016-08-18 Created: 2016-05-09 Last updated: 2017-06-13Bibliographically approved

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