Transfer-learning-based multi-wavelength laser sensor for high fidelity and real-time monitoring of ambient temperature and humidity

Abstract

Multi-wavelength laser absorption spectroscopy has the advantages of superior sensitivity, accuracy, and robustness for gas sensing applications, offering an opportunity for the development of high-performance laser-based hygrothermographs. However, accurate and fast determination of gas parameters from multiple spectral features can be quite challenging in the presence of large numbers of features, measurement noise, and increasing demands for real-time measurements. To address this challenge, we propose a transfer-learning-based multi-wavelength laser absorption sensor for the quantitative and simultaneous measurement of temperature and concentration of water vapor, with a focus on real-time monitoring of ambient temperature and relative humidity (RH). A spectral simulation based on the most-updated HITRAN database was employed as the dataset for model pre-training and transfer learning. The experimental dataset was obtained from absorption measurements using a distributed feedback laser that probed multiple water absorption features within the band of 7179−7186cm−1. To evaluate the sensor performance, mean absolute error, error distribution, and linearity were selected. In the presence of an insufficient experimental dataset for direct data training, the proposed transfer learning approach outperformed the traditional deep learning method with a lower prediction error of 0.14°C and 0.42% for temperature and RH, respectively, as compared to the values of 0.84°C and 0.66% obtained using the traditional deep learning method. Finally, the fast data post-processing performance of the proposed transfer learning approach was demonstrated in a field test against the conventional baseline fitting method.