Please use this identifier to cite or link to this item:
Title: Wind-independent estimation of gas source distance from transient features of metal oxide sensor signals
Author: Burgués Calderón, Javier
Marco Colás, Santiago
Keywords: Detectors de gasos
Processament de senyals
Aprenentatge automàtic
Anàlisi de sèries temporals
Gas detectors
Signal processing
Machine learning
Time-series analysis
Issue Date: Sep-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract: The intermittency of the instantaneous concentration of a turbulent chemical plume is a fundamental cue for estimating the chemical source distance using chemical sensors. Such estimate is useful in applications such as environmental monitoring or localization of fugitive gas emissions by mobile robots or sensor networks. However, the inherent low-pass filtering of metal oxide (MOX) gas sensors typically used in odor-guided robots and dense sensor networks due to their low cost, weight and size hinders the quantification of concentration intermittency. In this paper, we design a digital differentiator to invert the low-pass dynamics of the sensor response, thus obtaining a much faster signal from which the concentration intermittency can be effectively computed. Using a fast photo-ionization detector as a reference instrument, we demonstrate that the filtered signal is a good approximation of the instantaneous concentration in a real turbulent plume. We then extract transient features from the filtered signal the so-called ''bouts'' to predict the chemical source distance, focusing on the optimization of the filter parameters and the noise threshold to make the predictions robust against changing wind conditions. This represents an advantage over previous bout-based models which require wind measurements typically taken with expensive and bulky anemometers to produce accurate predictions. The proposed methodology is demonstrated in a wind tunnel scenario where a MOX sensor is placed at various distances downwind of an emitting chemical source and the wind speed varies in the range 10-34 cm/s. The results demonstrate that models optimized with our methodology can provide accurate source distance predictions at different wind speeds.
Note: Reproducció del document publicat a:
It is part of: IEEE Access, 2019, vol. 7, p. 140461-140469
Related resource:
ISSN: 2169-3536
Appears in Collections:Articles publicats en revistes (Electrònica)

Files in This Item:
File Description SizeFormat 
691711.pdf2.68 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons