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Augmented Switching Linear Dynamical System Model for Gas Concentration Estimation with MOX Sensors in an Open Sampling System
Div PMA, Dept Mech Engn, Katholieke Univ Leuven, Heverlee, Belgium.
Örebro universitet, Institutionen för naturvetenskap och teknik. (Centre for Applied Autonomous Sensor Systems AASS)ORCID-id: 0000-0003-0195-2102
Div PMA, Dept Mech Engn, Katholieke Univ Leuven, Heverlee, Belgium; , Sect CST, Dept Mech Engn, Eindhoven Univ Technol, Eindhoven, Netherlands .
Fac Engn Sci, Katholieke Univ Leuven, Heverlee, Belgium.
2014 (Engelska)Ingår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, nr 7, s. 12533-12559Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
2014. Vol. 14, nr 7, s. 12533-12559
Nyckelord [en]
metal oxide semiconductor sensor, gas sensing, Bayesian inference
Nationell ämneskategori
Kemi
Forskningsämne
Kemi
Identifikatorer
URN: urn:nbn:se:oru:diva-36458DOI: 10.3390/s140712533ISI: 000340035700069Scopus ID: 2-s2.0-84904178639OAI: oai:DiVA.org:oru-36458DiVA, id: diva2:743888
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Funding Agency:

KU Leuven OT project

Tillgänglig från: 2014-09-05 Skapad: 2014-09-05 Senast uppdaterad: 2018-08-27Bibliografiskt granskad

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