Quantitative Monitoring of Combustion Stability Based on Image Adversarial Convolutional Autoencoder

被引：0

作者：

Han Z. ^{[1
]}

Zeng W. ^{[1
]}

Tang X. ^{[1
]}

Wang Y. ^{[2
]}

Xu C. ^{[3
]}

机构：

[1] School of Information and Communication Engineering, Nanjing Institute of Technology, Jiangsu Province, Nanjing

[2] Jiangsu Dingjing Combine Power Engineering Co., Ltd., Jiangsu Province, Nanjing

[3] School of Energy and Environment, Southeast University, Jiangsu Province, Nanjing

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2024年 / 44卷 / 09期

关键词：

adversarial convolutional autoencoder; combustion stability; flame image; quantitative monitoring;

D O I：

10.13334/j.0258-8013.pcsee.222435

中图分类号：

学科分类号：

摘要：

Accurate monitoring of combustion stability is of great significance in optimizing the combustion state. Traditional combustion stability monitoring methods are not only highly dependent on prior expert knowledge, but also difficult to achieve quantitative evaluation. To overcome these limitations, a novel quantitative assessment method for combustion stability is proposed in this study. In this method, an adversarial convolutional autoencoder (ACAE) is established to extract deep features of the flame images, and a quantitative assessment index is applied for feature analysis. Especially, the ACAE adopts a novel adversarial mechanism to improve the training efficiency and thereby enhance the feature learning ability. The numerical interval of the quantitative assessment index is [0, 1], and when the assessment index is lower than 0.5, the combustion state is stable. The feasibility of the stability monitoring method is verified by ethylene combustion experiments, and the testing results confirm that the deep image features extracted by the ACAE can be used to quantitatively estimate the combustion stability. Furthermore, the proposed monitoring method has a strong generalization performance that can accurately identify flame images beyond the scope of the training dataset. © 2024 Chin.Soc.for Elec.Eng.

引用

页码：3610 / 3618

页数：8

共 26 条

[21] ZHANG Zhiyu, LI Xinli, NO<sub>x</sub> emission prediction of thermal power plant based on improved generative adversarial network pix2pix[J], Proceedings of the CSEE, 43, 12, pp. 4678-4687, (2023)
[22] QIN Huan, Quantitative analysis of furnace flame stability based on fire detection signal
[23] LI Xiaojie, Application research of multi-scale principal component analysis in pattern recognition of thermal system, (2019)
[24] Qi QIAN, Rong JIN, YI Jinfeng, Efficient distance metric learning by adaptive sampling and mini-batch Stochastic Gradient Descent(SGD)[J], Machine Learning, 99, 3, pp. 353-372, (2015)
[25] Zhezhe HAN, Jian LI, ZHANG Biao, Prediction of combustion state through a semi-supervised learning model and flame imaging[J], Fuel, 289, (2021)
[26] Duo SUN, Gang LU, ZHOU Hao, Condition monitoring of combustion processes through flame imaging and kernel principal component analysis [J], Combustion Science and Technology, 185, 9, pp. 1400-1413, (2013)

← 1 2 3 →