Analyzing the Response of a Temperature Modulated Tin-Oxide Gas Sensor Using Local Linear Neuro-Fuzzy Model for Gas Detection

A resistive gas sensor (RGS) under temperature modulation regime is considered as a system for gas detection. Five target gases including Methanol, Ethanol, 2-Propanol, 1-Butanol, and Hydrogen each at 11 concentration levels, were selected for diagnosis using a single commercial gas sensor. For modulating the sensor, a staircase containing five voltage steps each with 20s plateau is applied to micro-heater of the sensor. This, in turn, alters both the temperature and the resistance profiles of the sensing layer which are considered as the input and the output of the defined system, respectively. In this way, five systems corresponding to five steps of the system input can be distinguished. Next, each system under the influence of the examined target gases is modeled with neuro-fuzzy network. Local linear model tree (LOLIMOT) used as learning algorithm of the systems and weights of the trained networks utilized as the features of the sensor in presence of target gas. Mapping these feature vectors using linear discriminant analysis showed successful classification of all target gases.