Journal of Shanghai Jiao Tong University ›› 2021, Vol. 55 ›› Issue (4): 462-470.doi: 10.16183/j.cnki.jsjtu.2020.111

Special Issue: 《上海交通大学学报》2021年12期专题汇总专辑 《上海交通大学学报》2021年“能源与动力工程”专题

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Gas-Fired Flame Stability Based on Optical Flow Method and Deep Learning

WANG Yu, YU Yuefeng(), ZHU Xiaolei, ZHANG Zhongxiao   

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2020-04-15 Online:2021-04-28 Published:2021-04-30
  • Contact: YU Yuefeng


The stability of gas-fired flame is studied by combining the optical flow method and deep learning. The optical flow vector of the flame image is directly calculated by using the optical flow method. The pulsation of the flame in the two-dimensional image is observed, and an optical flow pulsation evaluation model is proposed to evaluate the stability of the flame. In addition, a deep convolutional neural network based on VGG-Nets is built and fine adjustments are made on ImageNet pre-training weights. Combining the static and dynamic characteristics of flames, the classification and recognition of five typical combustion states are achieved. The results show that this method has a good judgment ability for different combustion states of flames and a high recognition rate for unstable combustion flames.

Key words: gas-fired flame, stability, optical flow method, deep learning

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