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Augmented Switching Linear Dynamical System Model for Gas Concentration Estimation with MOX Sensors in an Open Sampling System
Katholieke Univ Leuven, Div PMA, Dept Mech Engn, Heverlee, Belgium.
Örebro University, School of Science and Technology. Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden;.ORCID iD: 0000-0003-0195-2102
Katholieke Univ Leuven, Div PMA, Dept Mech Engn, Heverlee, Belgium; Eindhoven Univ Technol, Sect CST, Dept Mech Engn, Eindhoven, Netherlands .
Katholieke Univ Leuven, Fac Engn Sci, Heverlee, Belgium.
2014 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 7, 12533-12559 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we introduce a Bayesian time series model approach for gas concentration estimation using Metal Oxide (MOX) sensors in Open Sampling System (OSS). Our approach focuses on the compensation of the slow response of MOX sensors, while concurrently solving the problem of estimating the gas concentration in OSS. The proposed Augmented Switching Linear System model allows to include all the sources of uncertainty arising at each step of the problem in a single coherent probabilistic formulation. In particular, the problem of detecting on-line the current sensor dynamical regime and estimating the underlying gas concentration under environmental disturbances and noisy measurements is formulated and solved as a statistical inference problem. Our model improves, with respect to the state of the art, where system modeling approaches have been already introduced, but only provided an indirect relative measures proportional to the gas concentration and the problem of modeling uncertainty was ignored. Our approach is validated experimentally and the performances in terms of speed of and quality of the gas concentration estimation are compared with the ones obtained using a photo-ionization detector.

Place, publisher, year, edition, pages
2014. Vol. 14, no 7, 12533-12559 p.
Keyword [en]
metal oxide semiconductor sensor, gas sensing, Bayesian inference
National Category
Chemical Sciences
Research subject
Chemistry
Identifiers
URN: urn:nbn:se:oru:diva-36458DOI: 10.3390/s140712533ISI: 000340035700069Scopus ID: 2-s2.0-84904178639OAI: oai:DiVA.org:oru-36458DiVA: diva2:743888
Note

Funding Agency:

KU Leuven OT project

Available from: 2014-09-05 Created: 2014-09-05 Last updated: 2017-12-05Bibliographically approved

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Trincavelli, Marco

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