Monitoring reliability of sensors in an array by neural networks

The correlation between the responses of five semiconductor thin films sensors to CO–NO2 mixtures is exploited to detect a possible malfunctioning of one of the sensors during operation. To this end, at every time instant, the current flowing in each single sensor is estimated as a function of the current flowing in the remaining ones. With multiple linear regression, we obtain, in the case of the worst sensor, a regression coefficient of 0.89. The estimation is then accomplished using the regression ability of five artificial neural networks (ANN), one for each sensor, obtaining at worst a mean estimation error on the test set of 6×10−3 μA2, the signal being of the order of the microampere (μA). In the case of a simulated transient malfunctioning, we show how it is possible to detect on-line which is the sensor that is not working properly. Further, after a fault has been detected, the estimation replaces the damaged sensor response. In this way, the concentration prediction — performed by other ANNs that need the responses of all the sensors — can proceed until the damaged sensor has been replaced.