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TREFEX: trend estimation and change detection in the response of mox gas sensors
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0003-0217-9326
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0003-0195-2102
2013 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 6, 7323-7344 p.Article in journal (Refereed) Published
Abstract [en]

Many applications of metal oxide gas sensors can benefit from reliable algorithmsto detect significant changes in the sensor response. Significant changes indicate a changein the emission modality of a distant gas source and occur due to a sudden change ofconcentration or exposure to a different compound. As a consequence of turbulent gastransport and the relatively slow response and recovery times of metal oxide sensors,their response in open sampling configuration exhibits strong fluctuations that interferewith the changes of interest. In this paper we introduce TREFEX, a novel change pointdetection algorithm, especially designed for metal oxide gas sensors in an open samplingsystem. TREFEX models the response of MOX sensors as a piecewise exponentialsignal and considers the junctions between consecutive exponentials as change points. Weformulate non-linear trend filtering and change point detection as a parameter-free convexoptimization problem for single sensors and sensor arrays. We evaluate the performanceof the TREFEX algorithm experimentally for different metal oxide sensors and severalgas emission profiles. A comparison with the previously proposed GLR method shows aclearly superior performance of the TREFEX algorithm both in detection performance andin estimating the change time.

Place, publisher, year, edition, pages
MDPI AG , 2013. Vol. 13, no 6, 7323-7344 p.
Keyword [en]
metal oxide sensors, open sampling system, change point detection, trend filtering
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-29340DOI: 10.3390/s130607323ISI: 000320767600026Scopus ID: 2-s2.0-84878868903OAI: oai:DiVA.org:oru-29340DiVA: diva2:625614
Available from: 2013-06-05 Created: 2013-06-05 Last updated: 2017-12-06Bibliographically approved

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Pashami, SepidehLilienthal, Achim J.Schaffernicht, ErikTrincavelli, Marco
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