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Multi-mode combustion process monitoring on a pulverised fuel combustion test facility based on flame imaging and random weight network techniques

机译：基于火焰成像和随机权重网络技术的粉状燃料燃烧测试设备的多模式燃烧过程监控

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摘要

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.
机译：燃烧系统需要在各种不同条件下运行，以满足不断变化的能源需求。在这种可变条件下，可靠地监控燃烧过程对于燃烧控制和优化至关重要。本文提出了一种结合数字成像，PCA-RWN（主成分分析和随机权重网络）技术的可变燃烧状态监测方法。基于使用数字成像系统获取的火焰图像，将基于灰度共生矩阵计算的RGB（红色，绿色和蓝色）图像分量的平均强度值和纹理描述符用作图像的颜色和纹理特征。火焰图像。这些功能被视为用于多模式过程监视的建议PCA-RWN模型的输入变量。在提出的模型中，PCA用于提取输入向量的主分量特征。通过为适当的主成分子空间建立RWN模型，可大大减少识别燃烧工况的计算量。另外，计算出相应操作条件的霍特林的T-2和SPE（平方预测误差）统计量，以识别燃烧异常。使用在PACT 250 kW空气/有氧燃料燃烧试验设施（PACT 250 kW空气/有氧燃料CTF）上获得的火焰图像数据集对提出的方法进行了评估。通过更改一次空气和SA / TA（二次空气到地区空气）的分配比例来实现可变的运行条件。结果表明，对于所检查的操作条件，所提出的PCA-RWN模型的条件识别成功率超过91％，在减少训练时间方面优于其他机器学习分类器。结果还表明，异常状况显示出与正常状况不同的振荡频率，并且T2和SPE统计信息能够检测到此类异常。 Crown版权所有（C）2017，由Elsevier Ltd.发布。

著录项

  • 来源
    《Fuel》 |2017年第15期|656-664|共9页
  • 作者

    Bai Xiaojing; Lu Gang; Hossain Md Moinul; Szuhanszki Janos; Daood Syed Sheraz; Nimmo William; Yan Yong; Pourkashanian Mohamed;

  • 作者单位

    North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China|Univ Kent, Sch Engn & Digital Arts, Canterbury CT2 7NT, Kent, England;

    Univ Kent, Sch Engn & Digital Arts, Canterbury CT2 7NT, Kent, England;

    Univ Kent, Sch Engn & Digital Arts, Canterbury CT2 7NT, Kent, England;

    Univ Sheffield, Dept Mech Engn, Energy Grp 2050, Sheffield S10 2TN, S Yorkshire, England;

    Univ Sheffield, Dept Mech Engn, Energy Grp 2050, Sheffield S10 2TN, S Yorkshire, England;

    Univ Sheffield, Dept Mech Engn, Energy Grp 2050, Sheffield S10 2TN, S Yorkshire, England;

    Univ Kent, Sch Engn & Digital Arts, Canterbury CT2 7NT, Kent, England;

    Univ Sheffield, Dept Mech Engn, Energy Grp 2050, Sheffield S10 2TN, S Yorkshire, England;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Fossil fuel combustion; Multi-mode process monitoring; Flame image; Principal components analysis; Random weight network;

    机译：化石燃料燃烧;多模式过程监控;火焰图像;主成分分析;随机重量网络;

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