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Scalable validation of industrial equipment using a functional DSMS
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2016 (English)In: Journal of Intelligent Information Systems, ISSN 0925-9902, E-ISSN 1573-7675, 1-25 p.Article in journal (Refereed) Epub ahead of print
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. © 2016 The Author(s)

Place, publisher, year, edition, pages
Springer-Verlag New York, 2016. 1-25 p.
Keyword [en]
Construction equipment; Data communication systems; Equipment; High level languages; Machinery; Query languages, Anomaly detection; Data stream; Data stream management; Functional data modeling; Industrial equipment; Parallelizations; Slippage detection; Volvo construction equipments, Information management
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-12975DOI: 10.1007/s10844-016-0427-2ScopusID: 2-s2.0-84982266338OAI: oai:DiVA.org:bth-12975DiVA: diva2:956887
Available from: 2016-08-31 Created: 2016-08-31 Last updated: 2016-09-05Bibliographically approved

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Håkansson, Lars
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Department of Applied Signal Processing
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Citation style
  • apa
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