Multi-mode combustion process monitoring on a pulverised fuel combustion test facility based on flame imaging and random weight network techniques
Multi-mode combustion process monitoring on a pulverised fuel combustion test facility based on flame imaging and random weight network techniques
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DOI：
10.1016/j.fuel.2017.03.091
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发表时间：
2017-08-15
期刊：
影响因子：
7.4
通讯作者：
Pourkashanian, Mohamed
中科院分区：
文献类型：
--
作者：
Bai, Xiaojing;Lu, Gang;Pourkashanian, Mohamed
燃烧系统需要在一系列不同的条件下运行，以满足波动的能源需求。燃烧过程的可靠监测对于在这种可变条件下的燃烧控制和优化至关重要。本文提出了一种结合数字图像、主成分分析和随机加权网络（PCA RWN）技术的变工况燃烧监测方法。基于使用数字成像系统获取的火焰图像，RGB（红、绿色和蓝色）图像分量的平均强度值和基于灰度共生矩阵计算的纹理描述符被用作火焰图像的颜色和纹理特征。这些功能被视为多模式过程监控的PCA-RWN模型的输入变量。在该模型中，使用PCA提取输入向量的主成分特征。通过选取合适的主分量子空间建立RWN模型，大大降低了燃烧工况识别的计算量。此外，Hotelling的T-2和SPE（平方预测误差）统计的相应的操作条件的计算，以识别燃烧的异常。所提出的方法进行评估，使用火焰图像数据集上获得的PACT 250千瓦空气/氧燃料燃烧试验设施（PACT 250千瓦空气/氧燃料CTF）。通过改变一次风和SA/TA（二次风到区域风）分流，实现了可变的运行条件。结果表明，对于检查的操作条件，所提出的PCA-RWN模型的条件识别成功率超过91%，这优于其他机器学习分类器，减少了训练时间。结果还表明，异常条件下表现出不同的振荡频率从正常条件下，和T2和SPE统计是能够检测到这样的异常。皇冠版权所有（C）2017由Elsevier Ltd.发布
Combustion systems need to be operated under a range of different conditions to meet fluctuating energy demands. Reliable monitoring of the combustion process is crucial for combustion control and optimisation under such variable conditions. In this paper, a monitoring method for variable combustion conditions is proposed by combining digital imaging, PCA-RWN (Principal Component Analysis and Random Weight Network) techniques. Based on flame images acquired using a digital imaging system, the mean intensity values of RGB (Red, Green, and Blue) image components and texture descriptors computed based on the grey-level co-occurrence matrix are used as the colour and texture features of flame images. These features are treated as the input variables of the proposed PCA-RWN model for multi-mode process monitoring. In the proposed model, the PCA is used to extract the principal component features of input vectors. By establishing the RWN model for an appropriate principal component subspace, the computing load of recognising combustion operation conditions is significantly reduced. In addition, Hotelling's T-2 and SPE (Squared Prediction Error) statistics of the corresponding operation conditions are calculated to identify the abnormalities of the combustion. The proposed approach is evaluated using flame image datasets obtained on the PACT 250 kW Air/Oxy-fuel Combustion Test Facility (PACT 250 kW Air/Oxy-fuel CTF). Variable operation conditions were achieved by changing the primary air and SA/TA (Secondary Air to Territory Air) splits. The results demonstrate that, for the operation conditions examined, the condition recognition success rate of the proposed PCA-RWN model is over 91%, which outperforms other machine learning classifiers with a reduced training time. The results also show that the abnormal conditions exhibit different oscillation frequencies from the normal conditions, and the T2 and SPE statistics are capable of detecting such abnormalities. Crown Copyright (C) 2017 Published by Elsevier Ltd.