A SYSTEMATIC INVESTIGATION ON THE USE OF TIME-DEPENDENT SENSOR SIGNALS IN SIGNAL-PROCESSING TECHNIQUES

Due to the lack of selectivity and separability of most common chemical sensors, the use of sensor arrays in combination with artificial neural network (ANN) signal processing is widely used. The time-dependent behaviour of gas sensors is one of the principal problems in signal-processing methods for sensor arrays. These time dependencies of semiconductor sensors can be classified into three main time domains: first, the rise time of the sensor after a sudden concentration change; secondly, the short-term drift after switching on; and finally, the long-term deterioration of the semiconductor material. An important improvement in the treatment of these time-dependent processes is the compensation of sensor rise time and short-term drift effects. The short-term drift of semiconductor sensors can be modelled and equalized by a preprocessing step. The rise-time effects can be reduced by measuring the sensor signals with a high measurement rate and training ANNs with these measurements. Preprocessing the sensor signal and applying the rise-time behaviour leads to an improvement of ANN generalization ability, and attains a minimum of prediction error after a minimum of time after gas exchange.