Strategy to expedite gas sensor calibration based on cyclic temperature operation and data augmentation

Abstract

The gas sensor calibration using data-driven approach requires a large volume of training data. The conventional method demands numerous measurements of response data, consuming substantial manpower and time resources. To expedite and streamline the sensor calibration and model deployment, this study proposes a cost-effective and practical calibration strategy combining data augmentation and data preprocessing. This strategy aims to minimize both the collection time and amount of measurement data necessary while ensuring high identification accuracy and generalization performance. The random cropping technique based on sliding sampling window is developed to expand the sample size by randomly and consecutively resampling new data from the limited raw response data. This approach allows any fixed-length sequence from the raw response curve to serve as the training and test sample, facilitating fast and real-time measurement for online detection and dynamic analysis. Furthermore, data preprocessing techniques using sequence normalization and fast Fourier transform are employed to extract the species and concentration features, thereby mitigating drift-like effects in the response data. The effectiveness of this strategy is demonstrated with real measurement data, showcasing significant improvements in identification accuracy and generalization performance compared to conventional methods.