摘要
A difficult issue restricting the development of gas sensors is multicomponent recognition. Herein, a gas-sensing (GS) microchip loaded with three gas-sensitive materials was fabricated via a micromachining technique. Then, a portable gas detection system was built to collect the signals of the chip under various decomposition products of sulfur hexafluoride (SF6). Through a stacked denoising autoencoder (SDAE), a total of five high-level features could be extracted from the original signals. Combined with machine learning algorithms, the accurate classification of 47 simulants was realized, and 5-fold cross-validation proved the reliability. To investigate the generalization ability, 30 sets of examinations for testing unknown gases were performed. The results indicated that SDAE-based models exhibit better generalization performance than PCA-based models, regardless of the magnitude of noise. In addition, hypothesis testing was introduced to check the significant differences of various models, and the bagging-based back propagation neural network with SDAE exhibits superior performance at 95% confidence.
基金
This work was supported by the State Grid Corporation of China through the Science and Technology Project under Grant 5500-201999543A-0-0-00.We also thank Mr.Zijun Ren at the Instrument Analysis Center of Xi’an Jiaotong University for his assistance with the FESEM analysis.