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基于混合数据增强的MSWI过程燃烧状态识别

郭海涛 汤健 丁海旭 乔俊飞

郭海涛, 汤健, 丁海旭, 乔俊飞. 基于混合数据增强的MSWI过程燃烧状态识别. 自动化学报, 2022, 48(x): 1001−1016 doi: 10.16383/j.aas.c210843
引用本文: 郭海涛, 汤健, 丁海旭, 乔俊飞. 基于混合数据增强的MSWI过程燃烧状态识别. 自动化学报, 2022, 48(x): 1001−1016 doi: 10.16383/j.aas.c210843
Guo Hai-Tao, Tang Jian, Ding Hai-Xu, Qiao Jun-Fei. Combustion states recognition method of mswi process based on mixed data enhancement. Acta Automatica Sinica, 2022, 48(x): 1001−1016 doi: 10.16383/j.aas.c210843
Citation: Guo Hai-Tao, Tang Jian, Ding Hai-Xu, Qiao Jun-Fei. Combustion states recognition method of mswi process based on mixed data enhancement. Acta Automatica Sinica, 2022, 48(x): 1001−1016 doi: 10.16383/j.aas.c210843

基于混合数据增强的MSWI过程燃烧状态识别

doi: 10.16383/j.aas.c210843
基金项目: 国家自然科学基金(62073006,62021003), 北京市自然科学基金资助项目(4212032, 4192009), 科学技术部国家重点研发计划(2018YFC1900800-5), 矿冶过程自动控制技术国家(北京市)重点实验室(BGRIMM-KZSKL-2020-02)资助
详细信息
    作者简介:

    郭海涛:北京工业大学信息学部硕士研究生. 主要研究方向为面向城市固废焚烧过程的图像处理研究等. E-mail: guoht@emails.bjut.edu.cn

    汤健:北京工业大学信息学部教授. 主要研究方向为小样本数据建模, 城市固废处理过程智能控制. 本文通信作者. E-mail: freeflytang@bjut.edu.cn

    丁海旭:北京工业大学信息学部博士研究生. 主要研究方向为城市固废焚烧过程特征建模与智能控制等. E-mail: dinghaixu@emails.bjut.edu.cn

    乔俊飞:北京工业大学信息学部教授. 主要研究方向为污水处理过程智能控制, 神经网络结构设计与优化. E-mail: junfeiq@bjut.edu.cn

Combustion States Recognition Method of MSWI process Based on Mixed Data Enhancement

Funds: Supported by National Natural Science Foundation of China(62073006,62021003), Beijing Natural Science Foundation (4212032,4192009),National Key R&D Program of the Ministry of Science and Technology(2018YFC1900800-5),Beijing Key Laboratory of Process Automation in Mining & Metallurgy (BGRIMM-KZSKL-2020-02)
More Information
    Author Bio:

    GUO Hai-Tao Master student at the Faculty of Information Technology, Beijing University of Technology. His research interest covers image processing of municipal solid waste incineration process

    TANG Jian Professor at Beijing University of Technology. His research interest covers small sample data modeling and intelligent control of municipal solid waste treatment process. Corresponding author of this paper

    DING Hai-Xu Ph. D. candidate at the Faculty of Information Technology, Beijing University of Technology. His research interest covers feature modeling and intelligent control of municipal solid waste incineration process

    QIAO Jun-Fei Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent control of wastewater treatment process, and structure design and optimization of neural networks

  • 摘要: 国内城市固废焚烧(Municipal solid waste incineration, MSWI)过程通常依靠运行专家观察炉内火焰识别燃烧状态后再结合自身经验修正控制策略以维持稳定燃烧, 存在智能化水平低、识别结果具有主观性与随意性等问题. 因MSWI过程的火焰图像具有强污染、多噪声等特性, 并且存在异常工况数据较为稀缺等问题, 导致传统目标识别方法难以适用. 对此, 本文提出了一种基于混合数据增强的MSWI过程燃烧状态识别方法. 首先, 结合领域专家经验与焚烧炉排结构对燃烧状态进行标定; 接着, 设计由粗调和精调两级组成的深度卷积生成对抗网络(Deep convolutional generative adversarial network, DCGAN)以获取多工况火焰图像; 然后, 采用弗雷歇距离(Fréchet inception distance, FID)对生成式样本进行自适应选择; 最后, 通过非生成式数据增强对样本进行再次扩充, 获得混合增强数据构建卷积神经网络以识别燃烧状态. 基于某MSWI电厂实际运行数据实验, 表明该方法有效地提高了识别网络的泛化性与鲁棒性, 具有良好的识别精度.
    1)  收稿日期 2019-01-09 录用日期 2019-06-24 Manuscript received September 6, 2021; accepted December 2,2021 国家自然科学基金 (62073006,62021003), 北京市自然科学基金资助项目 (4212032, 4192009), 科学技术部国家重点研发计划(2018YFC1900800-5), 矿冶过程自动控制技术国家 (北京市) 重点实验室 (BGRIMM-KZSKL-2020-02) 资助 Supported by National Natural Science Foundation of China (62073006,62021003), Beijing Natural Science Foundation (4212032,4192009), National Key R&D Program of the Ministry of Science and Technology (2018YFC1900800-5), Beijing Key Laboratory of Process Automation in Mining & Metallurgy (BGRIMM-KZSKL-2020-02)
    2)  本文责任编委  Recommended by Associate Editor 1. 北京工业大学信息学部 北京 100124 2. 智慧环保北京实验室 北京 100124 1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 2. Beijing Laboratory of Smart Environmental Protection, Beijing, 100124
  • 图  1  MSWI过程工艺图

    Fig.  1  Flow chart of MSWI process

    图  2  基于DCGAN数据增强的燃烧状态识别策略

    Fig.  2  Strategy of combustion state recognition based on DCGAN data enhancement

    图  3  炉排等比例结构示意图

    Fig.  3  Schematic diagram of equal proportion structure of grate

    图  4  燃烧和停炉状态图像标定示意图

    Fig.  4  Image calibration diagram of combustion and shutdown status

    图  5  生成网络结构

    Fig.  5  Structure of generation network

    图  6  判别网络结构

    Fig.  6  Structure of discrimination network

    图  7  燃烧线前移

    Fig.  7  Combustion line forward

    图  8  燃烧线正常

    Fig.  8  Combustion line normal

    图  9  燃烧线后移

    Fig.  9  Combustion line back

    图  10  粗调DCGAN迭代过程中FID对生成燃烧状态图像的评估结果

    Fig.  10  Assessment of FID for generating combustion state images during rough DCGAN iteration

    图  11  燃烧线前移的增强图像

    Fig.  11  Expansion results of combustion line forward image

    图  13  燃烧线后移的增强图像

    Fig.  13  Expansion results of combustion line back image

    图  12  燃烧线正常的增强图像

    Fig.  12  Expansion results of combustion line normal image

    图  14  非生成式数据增强

    Fig.  14  Non-generative data enhancement

    图  15  非生成式数据增强

    Fig.  15  Non-generative data enhancement

    图  16  不同生成模型生成的燃烧状态图像

    Fig.  16  Incineration state images generated by different generation models

    表  1  数据集划分

    Table  1  Data set partition

    划分方式训练集验证集测试集
    A方式时间段T1~T8T9T10
    数量9*89*19*1
    B方式随机9*89*19*1
    下载: 导出CSV

    表  2  不同生成模型生成数据的评估结果

    Table  2  Evaluation results of data generated by different generation models

    评价指标
    FIDMINFIDAVERAGEEpoch
    GAN250254.510000
    LSGAN58.5651.943000
    DCGAN43.8149.672500
    本文方法36.1048.512500
    下载: 导出CSV

    表  3  识别模型的性能对比

    Table  3  Performance comparison of recognition models

    方法测试集准确率测试集Loss验证集准确率验证集Loss
    A方式CNN0.7518±0.002450.6046±0.028820.6115±0.002121.6319±0.11640
    非生成数据增强+CNN0.8272±0.002060.6504±0.040380.7830±0.001830.9077±0.03739
    DCGAN数据增强+CNN0.8000±0.000980.8776±0.010630.5885±0.003961.9024±0.11050
    本文方法0.8482±0.001050.5520±0.010060.7269±0.003770.9768±0.05797
    B方式CNN0.8926±0.001050.2298±0.003090.8519±0.000610.2519±0.00167
    非生成数据增强+CNN0.9371±0.001840.1504±0.008250.9704±0.000550.1093±0.01037
    DCGAN数据增强+CNN0.9000±0.001230.3159±0.011500.8445±0.002070.2913±0.00396
    本文方法0.9407±0.003670.2019±0.014980.9741±0.000440.0699±0.00195
    下载: 导出CSV

    表    附表: 本文符号及含义表

    符号符号含义
    D判别器
    G生成器
    $ V(G,D)$GAN原始的目标函数
    下标r真实数据
    下标g生成数据
    z潜在空间的随机噪声
    $ D^*$固定G参数, 在$\mathop {\max }\nolimits_D V\left( {D,G} \right)$过程中, D的最优解
    ${D_{{\text{JS}}}}$JS散度
    ${R_{jk}}$图像中经过卷积核扫描后的第j行第k列的结果
    ${H_{j - u,k - v}}$图像
    ${F_{u,v}}$卷积核
    $X$燃烧状态数据集, 包含前移、正常和后移的数据集
    $ X_{{\rm{FW}}}$燃烧线前移数据集
    $ X_{{\rm{NM}}}$燃烧线正常数据集
    $ X_{{\rm{BC}}}$燃烧线后移数据集
    $ X'_{{\rm{FW}}}$训练集燃烧线前移数据集
    $ X'_{{\rm{NM}}}$训练集燃烧线正常数据集
    $ X'_{{\rm{BC}}}$训练集燃烧线后移数据集
    $ X''_{{\rm{FW}}}$测试、验证燃烧线前移数据集
    $ X''_{{\rm{NM}}}$测试、验证燃烧线正常数据集
    $ X''_{{\rm{BC}}}$测试、验证燃烧线后移数据集
    $ {D_t}(\cdot, \cdot )$燃烧图像粗调DCGAN子模块中判别网络参数为参数${\theta _{D,t}}$时, 判别网络预测值集合
    $ {D_{t+1}}(\cdot, \cdot )$燃烧图像粗调DCGAN子模块中, 判别网络参数为参数${\theta _{D,t+1}}$时, 判别网络预测值集合
    $ Y_{D,t}$在燃烧图像粗调DCGAN子模块中第t次博弈训练判别网络的真实值集合
    $ Y_{G,t}$在燃烧图像粗调DCGAN子模块中第t次博弈训练生成网络的真实值集合
    $ loss_{D,t}$在燃烧图像粗调DCGAN子模块中第t次博弈更新判别网络的损失值
    $ loss_{G,t}$在燃烧图像粗调DCGAN子模块中第t次博弈更新生成网络的损失值
    $ X_{{\rm{real}}}$在燃烧图像粗调DCGAN子模块中参加博弈的真实数据
    $ X_{{\rm{false}},t}$在燃烧图像粗调DCGAN子模块中参加第t次博弈的生成的数据
    $ G_t({\boldsymbol{z}})$在燃烧图像粗调DCGAN子模块第t次博弈中由随机噪声经过生成网络得到的虚拟样本
    ${S_{D,t}}$燃烧图像粗调DCGAN中获得的判别网络的结构参数;
    ${S_{G,t}}$燃烧图像粗调DCGAN中获得的生成网络的结构参数;
    ${\theta _{D,t}}$在燃烧图像粗调DCGAN子模块中第t次博弈判别网络更新前的网络参数
    ${\theta _{G,t}}$在燃烧图像粗调DCGAN子模块中第t次博弈生成网络更新前的网络参数
    $ X_{{\rm{real}}}^{{\rm{FW}}}$燃烧线前移精调DCGAN子模块中参加博弈的真实数据
    $ X_{{\rm{false}},t}^{{\rm{FW}}}$在燃烧线前移精调DCGAN子模块中参加第t次博弈的生成数据
    $ X_{{\rm{real}}}^{{\rm{NM}}}$燃烧线正常精调DCGAN子模块中参加博弈的真实数据
    $ X_{{\rm{false}},t}^{{\rm{NM}}}$在燃烧线正常精调DCGAN子模块中参加第t次博弈的生成数据
    $ X_{{\rm{real}}}^{{\rm{BC}}}$燃烧线后移精调DCGAN子模块中参加博弈的真实数据
    $ X_{{\rm{false}},t}^{{\rm{BC}}}$在燃烧线后移精调DCGAN子模块中参加第t次博弈的生成数据
    $ D_t^{{\rm{FW}}}(\cdot, \cdot )$在燃烧线前移精调DCGAN子模块中判别网络参数为参数$\theta _{D,t}^{{\text{FW}}}$时, 判别网络预测值集合
    $ D_t^{{\rm{NM}}}(\cdot, \cdot )$在燃烧线正常精调DCGAN子模块中判别网络参数为参数$\theta _{D,t}^{{\text{NM}}}$时, 判别网络预测值集合
    $ {D}_{t}^{\text{BC}}(\cdot, \cdot ) $在燃烧线后移精调DCGAN子模块中判别网络参数为参数$\theta _{D,t}^{{\text{BC}}}$时, 判别网络预测值集合
    $ D_{t+1}^{{\rm{FW}}}(\cdot, \cdot )$在燃烧线前移精调DCGAN子模块中判别网络参数为参数$\theta _{D,t + 1}^{{\text{FW}}}$时, 判别网络预测值集合
    $ D_{t+1}^{{\rm{NM}}}(\cdot, \cdot )$在燃烧线正常精调DCGAN子模块中判别网络参数为参数$\theta _{D,t + 1}^{{\text{NM}}}$时, 判别网络预测值集合
    $ D_{t+1}^{{\rm{BC}}}(\cdot, \cdot )$在燃烧线后移精调DCGAN子模块中判别网络参数为参数$\theta _{D,t + 1}^{{\text{BC}}}$时, 判别网络预测值集合
    $ Y_{D,t}^{{\rm{FW}}}$燃烧线前移精调DCGAN子模块中第t次博弈训练D的真实值集合;
    $ Y_{G,t}^{{\rm{FW}}}$燃烧线前移精调DCGAN子模块中第t次博弈训练G的真实值集合;
    $ Y_{D,t}^{{\rm{NM}}}$燃烧线正常精调DCGAN子模块中第t次博弈训练D的真实值集合;
    $ Y_{G,t}^{{\rm{NM}}}$燃烧线正常精调DCGAN子模块中第t次博弈训练G的真实值集合;
    $ Y_{D,t}^{{\rm{BC}}}$燃烧线后移精调DCGAN子模块中第t次博弈训练D的真实值集合;
    $ Y_{G,t}^{{\rm{BC}}}$燃烧线后移精调DCGAN子模块中第t次博弈训练G的真实值集合;
    $ loss_{D,t}^{{\rm{FW}}}$燃烧线前移精调DCGAN子模块中第t次博弈更新D的损失值;
    $ loss_{G,t}^{{\rm{FW}}}$燃烧线前移精调DCGAN子模块中第t次博弈更新G的损失值;
    $ loss_{D,t}^{{\rm{NM}}}$燃烧线正常精调DCGAN子模块中第t次博弈更新D的损失值;
    $ loss_{G,t}^{{\rm{NM}}}$燃烧线正常精调DCGAN子模块中第t次博弈更新G的损失值;
    $ loss_{D,t}^{{\rm{BC}}}$燃烧线后移精调DCGAN子模块中第t次博弈更新D的损失值;
    $ loss_{G,t}^{{\rm{BC}}}$燃烧线后移精调DCGAN子模块中第t次博弈更新G的损失值;
    $\theta _{D,t}^{{\text{FW}}}$燃烧线前移DCGAN子模块中第t次博弈判别网络更新前的网络参数
    $\theta _{G,t}^{{\text{FW}}}$燃烧线前移DCGAN子模块中第t次博弈生成网络更新前的网络参数
    $\theta _{D,t}^{{\text{NM}}}$燃烧线正常DCGAN子模块中第t次博弈判别网络更新前的网络参数
    $\theta _{G,t}^{{\text{NM}}}$燃烧线正常DCGAN子模块中第t次博弈生成网络更新前的网络参数
    $\theta _{D,t}^{{\text{BC}}}$燃烧线后移DCGAN子模块中第t次博弈判别网络更新前的网络参数
    $\theta _{G,t}^{{\text{BC}}}$燃烧线后移DCGAN子模块中第t次博弈生成网络更新前的网络参数
    $ Y_{{\rm{CNN}},t}$燃烧状态识别模块第t次更新CNN模型预测值集合
    ${\widehat Y_{{\text{CNN}},t}}$燃烧状态识别模块第t次更新CNN模型预测值集合
    $los{s_{{\text{CNN}},t}}$燃烧状态识别模块第t次更新CNN的损失
    ${\widehat Y_{{\text{CNN}},t}}$燃烧状态识别模块第t次更新CNN模型预测值集合
    $los{s_{{\text{CNN}},t}}$燃烧状态识别模块第t次更新CNN的损失
    $ \theta _{{\rm{CNN}},t}$燃烧状态识别模块第t次更新CNN的网络更新参数
    $ loss$神经网络的损失
    X燃烧图像粗调DCGAN中判别网络神输入值集合$ \left[ {{{\boldsymbol{x}}_{\boldsymbol{1}}};{{\boldsymbol{x}}_{\boldsymbol{2}}};{{\boldsymbol{x}}_{\boldsymbol{3}}}; \cdots ;{{\boldsymbol{x}}_a} \cdots } \right]$即$ \left[ {{X_{{\rm{real}}}};{X_{{\rm{false}}}}} \right]$
    $ {\boldsymbol{x}}_{\boldsymbol{a}}$神经网络第a张输入图像
    yaa张输入图像输入神经网络后的输出值
    $ D_t(X)$判别网络预测值集合即$ {D_t}(\cdot, \cdot )$,
    L损失函数
    δii层的误差
    Oii层输出
    Wii层的所有权重参数
    Bii层的所有偏置参数
    $ {\nabla _{{W_{i - 1}}}}$i−1层的权重的当前梯度
    $ {\nabla _{{B_{i - 1}}}}$i−1层的偏置的当前梯度
    $ {\theta _{D,t}}$t次判别网络的参数
    $ {m _{D,t}}$t次判别网络一阶动量
    $ {v _{D,t}}$t次判别网络的二阶动量
    α学习率
    γ很小的正实数
    $ {\nabla _{D,t}}$t次判别网络参数的梯度
    β1Adam超参数
    β2Adam超参数
    $ {\eta _{D,t}}$计算第t次的下降梯度
    $ {\widehat m_{D,t}}$初始阶段判别网络的第t次一阶动量
    $ {\widehat v_{D,t}}$初始阶段判别网络的第t次的二阶动量
    Y神经网络真值集合
    $ f(X)$神经网络预测值集合.
    常见数学符号公式说明
    ${\text{E}}$期望
    p概率分布
    ${p_{\text{r}}}$真实图像的概率分布
    ${p_{\text{g}}}$生成图像的概率分布
    ${p_{\boldsymbol{z}}}$z所服从的正态分布
    上标T转置
    Cov协方差矩阵
    ${{\rm{T}}} _{\text{r}}^{}$真实图像构成矩阵的迹
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-06
  • 录用日期:  2021-12-02
  • 网络出版日期:  2022-02-10

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