Transfer Learning to Significantly Reduce the Calibration Time of MOS Gas Sensors

Abstract

Metal oxide semiconductor (MOS) gas sensors used for indoor air quality monitoring require an intensive calibration to accurately quantify volatile organic compounds at ppb (parts per billion) level in complex gas mixtures. With the help of advances in the field of deep learning, particularly the use of convolutional neural networks together with neural architecture search, the noise of the quantification model can be reduced significantly with an uncertainty for xylene of 27 ppb. However, the calibration takes several days up to now. In this work, the concept of transfer learning is studied to reduce the required calibration time. It is shown that the calibration time of single sensors can be reduced by 96 %. The resulting uncertainty is only 21 ppb worse than the absolute best, i.e. the value for a complete individual calibration, which is sufficiently good. By slightly increasing the calibration time for transfer learning to 30 % of the initial time, an uncertainty value for xylene quantification of 36.3 ppb was achieved, and thus only 9.7 ppb worse than the best possible model.