Gas Recognition under Sensor Drift by Using Deep Learning

Machine olfaction is an intelligent system that combines a cross‐sensitivity chemical sensor array and an effective pattern recognition algorithm for the detection, identification, or quantification of various odors. Data collected by the sensor array are the multivariate time series signals with a complex structure, and these signals become more difficult to analyze due to sensor drift. In this work, we focus on improving the classification performance under sensor drift by using the deep learning method, which is popular nowadays. Compared with other methods, our method can effectively tackle sensor drift by automatically extracting features, thus not only removing the complexity of designing the hand‐made features but also making it pervasive for a variety of application in machine olfaction. Our experimental results show that the deep learning method can learn the features that are more robust to drift than the original input and achieves high classification accuracy.