Dynamic signal processing for gas sensors based on Hilbert-Huang Transform

Temperature modulation has been proved to be an efficient technique for improving the selectivity and stability of gas sensors. By retrieving information from dynamic signals, new response features are obtained that confer more selectivity to metal oxide sensors. Time-frequency and transient analysis have been widely used in this kind of dynamic signal processing. These methods represent important characteristics of a signal in both time and frequency domain. In this way, essential features of the signal can be viewed and analyzed in order to identify or quantify the detected gases. Very often the fast Fourier transform and the discrete wavelet transform have been used as feature extraction tools. This work presents the application of a new signal processing technique, empirical mode decomposition and the Hilbert spectrum, in analysis of dynamic response signals of gas sensors. Using EMD method, the dynamic signals were decomposed into the intrinsic modes that coexist in the sensor system, and to have a better understanding of the nature of the gas sensing response information contained in the sensor response signals. The experimental results show that marginal spectrum can be used as useful feature for identification. By this method, the intrinsic response components to the analyte may be provided and the extracted features are simple and having intrinsic physical meaning.