Explosive gas recognition system using thick film sensor array and neural network

A sensor array with nine discrete sensors integrated on a substrate was dev eloped for recognizing the species and quantity of explosive gases such as methane, propane, and butane. The sensor array consisted of nine oxide semi conductor gas-sensing materials with SnO2 as the base material plus a heati ng element based on a meandered platinum layer all deposited on the sensor. The sensors on the sensor array were designed to produce a uniform thermal distribution and show a high and broad sensitivity and reproductivity to l ow concentrations through the use of nano-sized sensing materials with high surface areas and different additives. Using the sensitivity signals of th e array along with an artificial neural network, a gas recognition system w as then implemented for the classification and identification of explosive gases. The characteristics of the multi-dimensional sensor signals obtained from the nine sensors were analyzed using the principal component analysis (PCA) technique, and a gas pattern recognizer was implemented using a mult i-layer neural network with an error back propagation learning algorithm. T he simulation and experimental results demonstrate that the proposed gas re cognition system is effective in identifying explosive gases. For real time processing, a DSP board (TMS320C31) was then used to implement the propose d gas recognition system in conjunction with a neural network. (C) 2000 Els evier Science B.V. All rights reserved.