Combustion States Recognition Method of MSWI Process Based on Mixed Data Enhancement
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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. 由于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, 提出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.
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关键词:
- 城市固废焚烧 /
- 深度卷积生成对抗网络 /
- 燃烧状态识别 /
- 非生成式数据增强 /
- 混合数据增强
Abstract: The municipal solid waste incineration (MSWI) process usually relies on operating experts to observe the flame inside furnace for recognizing the combustion states. Then, by combining the experts＇ own experience to modify the control strategy to maintain the stable combustion. Thus, this manual mode has disadvantages of low intelligence and the subjectivity and randomness recognition results. The traditional methods are difficult to apply to the MSWI process, which has the characteristics of strong pollution, multiple noise, and scarcity of samples under abnormal conditions. To solve the above problems, a combustion states recognition method of MSWI process based on mixed data enhancement is proposed. Firstly, combustion states are labeled by combining the experience of domain experts and the design structure of furnace grate. Next, a deep convolutional generative adversarial network (DCGAN) consisting of two levels of coarse and fine-tuning was designed to acquire multi-situation flame images. Then, the Fréchet inception distance (FID) is used to adaptively select generated samples. Finally, the sample features are enriched at the second time by using non-generative data enhancement strategy, and a convolutional neural network is constructed based on the mixed enhanced data to recognize the combustion state. Experiments based on actual operating data of a MSWI plant show that this method effectively improves the generalization and robustness of the recognition network and has good recognition accuracy. -
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 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 16 不同生成模型生成的燃烧状态图像
Fig. 16 Combustion state images generated by different generation models
表 1 数据集划分
Table 1 Dataset partition
数据集 划分方式 训练集 验证集 测试集 A 时间次序 9 × 8 9 × 1 9 × 1 B 随机抽样 9 × 8 9 × 1 9 × 1 
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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
方法 评价指标 FIDmin FIDaverage Epoch GAN 250.00 254.50 10000 LSGAN 58.56 51.94 3000 DCGAN 43.81 49.67 2500 本文方法 36.10 48.51 2500
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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 测试集损失 验证集准确率 验证集损失 方式A CNN 0.7518±0.00245 0.6046±0.02882 0.6115±0.00212 1.6319±0.11640 非生成式数据增强+CNN 0.8272±0.00206 0.6504±0.04038 0.7830±0.00183 0.9077±0.03739 DCGAN数据增强+CNN 0.8000±0.00098 0.8776±0.01063 0.5885±0.00396 1.9024±0.11050 本文方法 0.8482±0.00105 0.5520±0.01006 0.7269±0.00377 0.9768±0.05797 方式B CNN 0.8926±0.00105 0.2298±0.00309 0.8519±0.00061 0.2519±0.00167 非生成式数据增强+CNN 0.9371±0.00184 0.1504±0.00825 0.9704±0.00055 0.1093±0.01037 DCGAN数据增强+CNN 0.9000±0.00123 0.3159±0.01150 0.8445±0.00207 0.2913±0.00396 本文方法 0.9407±0.00367 0.2019±0.01498 0.9741±0.00044 0.0699±0.00195
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A1 符号及含义
A1 Symbols and their descriptions
符号 符号含义 D 判别器 G 生成器 $ V(D,G)$ GAN 原始的目标函数 ${\boldsymbol{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 ${H_{j - u,k - v}}$ 卷积核 ${F_{u,v}}$ 图像 $X$ 燃烧状态数据集, 包含前移、正常和后移的数据集, 即燃烧图像粗调 DCGAN 中判别网络输入值集合$[ { {\boldsymbol{x} }_{{1} } };{ {\boldsymbol{x} }_{{2} } }; $ ${ {\boldsymbol{x} }_{{3} } }; \cdots ;{ {\boldsymbol{x} }_{\rm{a}}} \cdots ]$, 即$ \left[ {{X_{{\rm{real}}}};{X_{{\rm{false}}}}} \right]$ $ 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${\theta _{D,t}}$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${\theta _{D,t+1}}$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 ${\widehat Y_{{\text{ CNN }},t}}$ 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 ${\boldsymbol{x} }_{{a} }$ 神经网络第 a 幅输入图像 $y_a $ 第 a 幅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 $\delta_i $ 第 i 层的误差 $O_i $ 第 i 层输出 $W_i$ 第 i 层的所有权重参数 $B_i $ 第 i 层的所有偏置参数 $ {\nabla _{{W_{i - 1}}}}$ 第$i-1 $层的权重的当前梯度 $ {\nabla _{{B_{i - 1}}}}$ 第$i-1 $层的偏置的当前梯度 $ {\theta _{D,t}}$ 第 t 次判别网络的参数 $ {m _{D,t}}$ 第 t 次判别网络一阶动量 $ {v _{D,t}}$ 第 t 次判别网络的二阶动量 $\alpha $ 学习率 $\gamma $ 很小的正实数 $ {\nabla _{D,t}}$ 第 t 次判别网络参数的梯度 $\beta_1 $ Adam 超参数 $\beta_2 $ Adam 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 $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 Cov 协方差矩阵
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