Multicomponent SF6 decomposition product sensing with a gas-sensing microchip

Author:

Chu Jifeng,Yang Aijun,Wang Qiongyuan,Yang Xu,Wang Dawei,Wang Xiaohua,Yuan Huan,Rong Mingzhe

Abstract

AbstractA 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.

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Condensed Matter Physics,Materials Science (miscellaneous),Atomic and Molecular Physics, and Optics

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