Accurate temperature prediction with small absorption spectral data enabled by transfer machine learning

Abstract

It is of great interest to explore the possibility of applying machine learning methods for tunable diode laser absorption spectroscopy (TDLAS). Conventional supervised machine learning could be used for accurate determination of gas properties such as temperature or concentration in TDLAS. However, it becomes quite challenging when there is only a small amount of measured data. In this work, we propose a transfer machine learning (TML) model for accurate temperature prediction from a small amount of measured data. In the experiment, a two-line thermometer is developed by exploiting H2O absorption lines centered at 1392 and 1371 nm. Based on the limited data of measured laser transmission and an easily obtained large amount of calculated absorption spectra, the proposed model attempts to reduce the feature difference between these two distinct types of datasets and then leverages the large labeled calculated data to build an accurate predictor for the unlabeled measured data. The TML method can achieve a temperature prediction with a mean absolute error of 0.02-0.77 K over the temperature range of 288-338 K and a Pearson correlation coefficient of -0.084 to 0.981.