基于卷积自编码的火焰图像稳定性定量评估

王安; 温武斌; 刘宏文; 韩哲哲; 许传龙

doi:10.19805/j.cnki.jcspe.2025.230781

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基于卷积自编码的火焰图像稳定性定量评估

基础研究 | 更新时间：2026-02-02

- 基于卷积自编码的火焰图像稳定性定量评估
- Quantitative Evaluation of Flame Image Stability Based on Convolutional Autoencoder
- 动力工程学报 2025年45卷第3期页码：325-333
- 作者机构：
  
  1. 国能常州发电有限公司,江苏,常州,213033
  2. 国家能源集团江苏电力有限公司,江苏,南京,215433
  3. 南京工程学院信息与通信工程学院,江苏,南京,211167
  4. 东南大学能源与环境学院,江苏,南京,210096
- 作者简介：
  
  [ "王安(1981—),男,江苏泰州人,高级工程师,本科,研究方向为燃烧优化、燃煤电厂智能化" ]
- 基金信息：
  
  国家重点研发计划资助项目(2023YFB4102904);国家自然科学基金资助项目(51976038);国家能源投资集团科技项目(GC-2023-075)
- DOI：10.19805/j.cnki.jcspe.2025.230781
  中图分类号： X924.2;TP391.41
- 网络出版：2025-03-15，
  
  纸质出版：2025
- 稿件说明：
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王安,温武斌,刘宏文,韩哲哲,许传龙. 基于卷积自编码的火焰图像稳定性定量评估动力工程学报, 2025, 45(3): 325-333 https://doi.

org/10.19805/j.cnki.jcspe.2025.230781
王安,温武斌,刘宏文,韩哲哲,许传龙. 基于卷积自编码的火焰图像稳定性定量评估动力工程学报, 2025, 45(3): 325-333 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.230781.

org/10.19805/j.cnki.jcspe.2025.230781 DOI：

摘要

提出了一种火焰图像稳定性定量评估方法

首先采用卷积自编码对火焰图像进行特征提取

然后利用定量评估指标予以特征分析。定量评估指标建立在图像特征的聚类分析和统计分析基础上

其数值区间为[0

1]。卷积自编码采用一种基于重建相似性的新损失函数

以提高训练效率。同时

在乙烯燃烧平台上开展试验研究

以验证火焰图像稳定性定量评估方法的有效性。结果表明:卷积自编码能够以无监督方式提取火焰图像特征

其性能明显优于传统特征学习方法;所建立的定量评估指标可以量化表征火焰图像稳定性

展现出极强的泛化能力。

Abstract

A quantitative evaluation method for flame image stability was proposed. First

convolutional autoencoder was used to extract features from flame images

and then quantitative evaluation index was employed for feature analysis. The quantitative evaluation index

with a numerical interval of [0

was established based on cluster analysis and statistical analysis of the image feature. The convolutional autoencoder adopted a novel loss function based on reconstruction similarity to improve training efficiency. The effectiveness of the quantitative evaluation method for flame image stability was verified through experiments on the ethylene combustion platform. Results show that the convolutional autoencoder can extract image features in an unsupervised manner

and its performance is obviously superior to traditional feature learning methods. In addition

the established quantitative evaluation index can quantitatively characterize the flame image stability

showing strong generalization ability.

关键词

Keywords

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