Contrastive domain generalization convolution neural network correcting the drift of gas sensors

The drift problems of gas sensors caused by aging ingredients and unexpected environmental impacts, which can severely decrease the performance and service life of electronic noses, have been widely discussed. Many scholars believe that the essential reason for sensor drift can be attributed to the different data distributions in the latent feature space. However, traditional drift compensation methods like OSC, GasNet, SniffMultinose, and SniffConv are costly and complex. This paper has introduced an algorithm called contrastive domain generalization convolution neural network (CDCNN) to resolve this problem. For the first time, domain generalization and contrastive learning were used as the drift compensation methods for the gas sensor. A novel data augmentation was proposed to enrich the datasets. Feature generation module is used to simulate the drift of gas sensors. Contrastive learning adapts to the unseen areas in the latent feature space. The artificially generated features and the original features are drawn closer in the feature space to improve the algorithm's generalization ability. Experiments on long-term drift data show that CDCNN achieves high accuracy (0.7230). The experimental results show that the CDCNN algorithm is more suitable for practical applications due to less resource consumption and looser constraints.