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rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO Lab)ORCID-id: /0000-0003-3272-4145
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO Lab)ORCID-id: 0000-0003-0217-9326
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO Lab)
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO Lab)ORCID-id: 0000-0003-0195-2102
2014 (engelsk)Inngår i: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, nr 6/7, s. 1123-1127Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
American Scientific Publishers , 2014. Vol. 12, nr 6/7, s. 1123-1127
Emneord [en]
MOX Sensor, Open Sampling System, Change Point Detection, Reweighted Norm Minimization
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:oru:diva-38056DOI: 10.1166/sl.2014.3170Scopus ID: 2-s2.0-84911386978OAI: oai:DiVA.org:oru-38056DiVA, id: diva2:758138
Tilgjengelig fra: 2014-10-24 Laget: 2014-10-24 Sist oppdatert: 2020-01-29bibliografisk kontrollert

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