文献类型：期刊文献

英文题名：Augmented flame image soft sensor for combustion oxygen content prediction

作者：Gao, Shuang[1];Dai, Yun[2];Li, Yingjie[3];Jiang, Yuxin[2];Liu, Yi[2]

机构：[1]Shaoxing Univ, Sch Mech & Elect Engn, Shaoxing 312000, Peoples R China;[2]Zhejiang Univ Technol, Inst Proc Equipment & Control Engn, Hangzhou 310023, Peoples R China;[3]Zhejiang Co Ltd, Huzhou Branch, China Mobile Commun Grp, Huzhou 313000, Peoples R China

年份：2023

卷号：34

期号：1

外文期刊名：MEASUREMENT SCIENCE AND TECHNOLOGY

收录：SCI-EXPANDED(收录号：WOS:000869990200001)、、WOS

基金：This research was funded by the National Natural Science Foundation of China (Grant Nos. 62022073 and 61873241) and the Natural Science Foundation of Zhejiang Province (Grant No. LQ22E090007) for their financial support.

语种：英文

外文关键词：oxygen content prediction; regression generative adversarial network; imbalanced learning; convolutional neural network; deep learning

外文摘要：Oxygen content is one of the most critical factors for high-efficiency combustion. Online measurement of oxygen content from flame images is important but still challenging. For construction of an oxygen content prediction model, most current feature extraction methods are not straightforward. Additionally, there are always sufficient data for common operating conditions in practice, while only limited data for other operating conditions. The data collection process for model training is costly and time-consuming. To tackle the problem, this work presents an augmented flame image soft sensor for automated combustion oxygen content prediction. A convolutional neural network (CNN) regression model is designed to predict the oxygen content directly from flame images, without a single feature extraction process. Moreover, a regression generative adversarial network with gradient penalty is proposed to generate flame images with oxygen content labels. It overcomes the imbalanced and insufficient data problem arising in the CNN regression model training. The proposed soft sensor is compared with several common regression methods for oxygen content prediction. Experimental results show that the proposed method can predict the combustion oxygen content with high accuracy from flame images although the original datasets are imbalanced.