Next Article in Journal
Synthesis and Characterization of a Mg2+-Selective Fluorescent Probe
Previous Article in Journal
A Study on the Clustering Technology of Underwater Isomorphic Sensor Networks Based on Energy Balance
Sensors 2014, 14(7), 12533-12559; doi:10.3390/s140712533

Augmented Switching Linear Dynamical System Model for Gas Concentration Estimation with MOX Sensors in an Open Sampling System

1,* , 2
1 Department of Mechanical Engineering, Division PMA, KU Leuven, BE-3001 Heverlee, Belgium 2 Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro SE-70182, Sweden 3 Faculty of Engineering Sciences, KU Leuven, BE-3001 Heverlee, Belgium 4 Department of Mechanical Engineering, Section CST, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
* Author to whom correspondence should be addressed.
Received: 26 May 2014 / Revised: 1 July 2014 / Accepted: 4 July 2014 / Published: 11 July 2014
(This article belongs to the Section Chemical Sensors)
View Full-Text   |   Download PDF [1454 KB, uploaded 11 July 2014]   |   Browse Figures


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.
Keywords: metal oxide semiconductor sensor; gas sensing; Bayesian inference metal oxide semiconductor sensor; gas sensing; Bayesian inference
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote |
MDPI and ACS Style

Di Lello, E.; Trincavelli, M.; Bruyninckx, H.; De Laet, T. Augmented Switching Linear Dynamical System Model for Gas Concentration Estimation with MOX Sensors in an Open Sampling System. Sensors 2014, 14, 12533-12559.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert