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rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors
Örebro University, School of Science and Technology. (AASS MRO Lab)
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
Örebro University, School of Science and Technology. (AASS MRO Lab)
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0195-2102
2014 (English)In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6/7, 1123-1127 p.Article in journal (Refereed) Published
Abstract [en]

 The detection of changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applications such as gas leak detection in mines or large-scale pollution monitoring where it is impractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances, it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach is that a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The algorithm proposed in this paper, rTREFEX, is an extension of the previously proposed TREFEX algorithm. rTREFEX interprets the sensor response by fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials, which has to be kept as low as possible, is determined automatically using an iterative approach that solves a sequence of convex optimization problems based on l1-norm. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and the gas source is delivered to the sensors exploiting natural advection and turbulence mechanisms. rTREFEX is compared against the previously proposed TREFEX, which already proved superior to other algorithms.

Place, publisher, year, edition, pages
American Scientific Publishers , 2014. Vol. 12, no 6/7, 1123-1127 p.
Keyword [en]
MOX Sensor, Open Sampling System, Change Point Detection, Reweighted Norm Minimization
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-38056DOI: 10.1166/sl.2014.3170Scopus ID: 2-s2.0-84911386978OAI: oai:DiVA.org:oru-38056DiVA: diva2:758138
Available from: 2014-10-24 Created: 2014-10-24 Last updated: 2017-10-03Bibliographically approved

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

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