面向全量测点耦合结构分析与估计的工业过程监测方法

赵健程; 赵春晖

doi:10.16383/j.aas.c220090

面向全量测点耦合结构分析与估计的工业过程监测方法

doi: 10.16383/j.aas.c220090

赵健程^{1, 2,},
赵春晖^{1, 2,}

1.
浙江大学控制科学与工程学院工业控制技术国家重点实验室杭州 310027
2.
浙江大学NGICS大平台杭州 310027

基金项目: 国家自然科学基金杰出青年基金 (62125306), 工业控制技术国家重点实验室自主课题 (ICT2021A15), 中央高校基本科研业务费专项资金资助(浙江大学NGICS大平台)资助

详细信息

作者简介:
赵健程：浙江大学控制科学与工程学院博士研究生. 2021年获得浙江大学控制科学与工程学院自动化专业学士学位. 主要研究方向为大数据分析, 深度学习和零样本学习. E-mail: zhaojiancheng@zju.edu.cn

赵春晖：浙江大学控制科学与工程学院教授. 2003年获得中国东北大学自动化专业学士学位, 2009年获得中国东北大学控制理论与控制工程专业博士学位, 先后在中国香港科技大学、美国加州大学圣塔芭芭拉分校做博士后研究工作. 主要研究方向为机器学习, 工业大数据解析与应用, 包括化工, 能源以及医疗领域. 本文通信作者. E-mail: chhzhao@zju.edu.cn

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出版历程
- 收稿日期: 2022-02-08
- 录用日期: 2022-09-06
- 网络出版日期: 2022-10-08

An Industrial Process Monitoring Method Based on Total Measurement Point Coupling Structure Analysis and Estimation

ZHAO Jian-Cheng^{1, 2
,},
ZHAO Chun-Hui^{1, 2
,}

1.
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027
2.
Zhejiang University NGICS Platform, Hangzhou 310027

Funds: Supported by National Natural Science Foundation of China (62125306), the Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (ICT2021A15), and the Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform)

More Information

Author Bio:
ZHAO Jian-Cheng　Ph.D. candidate at the College of Control Science and Engineering, Zhejiang University. He received the B.Eng. degree from the College of Control Science and Engineering, Zhejiang University in 2021. His research interest covers big data analysis, deep learning, and zero-shot learning

ZHAO Chun-Hui　Professor at the College of Control Science and Engineering, Zhejiang University. She received her bachelor, master, and Ph.D. degrees from the Department of Automation, Northeastern University, Shenyang, China in 2003, 2006, and 2009, respectively. She was a postdoctoral fellow (January 2009-January 2012) at the Hong Kong University of Science and Technology, China and the University of California, Santa Barbara, Los Angeles, CA, USA. Her research interest covers machine learning, analytics of industrial big data, and their applications in energy and medical fields. Corresponding author of this paper

摘要

摘要: 实际工业场景中, 需要在生产过程中收集大量测点的数据, 从而掌握生产过程运行状态. 传统的过程监测方法通常仅评估运行状态整体的异常与否, 或对运行状态进行分级评估, 这种方式并不会直接定位故障部位, 不利于故障的高效检修. 为此, 提出了一种基于全量测点估计的监测模型, 根据全量测点估计值与实际值的偏差定义监测指标, 从而实现全量测点的分别精准监测. 为了克服原有的基于工况估计的监测方法监测不全面且对测点间耦合关系建模不充分的问题, 提出了多核图卷积网络(Multi-kernel graph convolution network, MKGCN), 通过将全量传感器测点视为一张全量测点图, 显式地对测点间耦合关系进行建模, 从而实现了全量传感器测点的同步工况估计. 此外, 面向在线监测场景, 设计了基于特征逼近的自迭代方法, 从而克服了在异常情况下由于测点间强耦合导致的部分测点估计值异常的问题. 所提出的方法在电厂百万千瓦超超临界机组中引风机的实际数据上进行了验证, 结果显示, 提出的监测方法与其他典型方法相比能够更精准地检测出发生故障的测点.
- 自迭代特征替换 /
- 多核图卷积 /
- 全量测点估计 /
- 故障检测
Abstract: In the actual industrial scenario, it is necessary to collect a large number of data from measuring points in the production process, so as to master the state of the production process. Traditional process monitoring methods usually only evaluate whether the overall operation state is abnormal or not, or carry out hierarchical evaluation of the state. These methods don’t directly locate the fault location, which is not conducive to the efficient maintenance of the fault. Therefore, in this paper, a monitoring model based on total measurement point estimation is proposed, and the monitoring indicators are defined according to the deviation between the estimated value and the actual value of total measurement points, so as to realize the separate and accurate monitoring of total measurement points. In order to overcome the problems of incomplete monitoring and insufficient modeling of coupling relationship between measuring points in the original monitoring method based on condition estimation, a multi-kernel graph convolution network (MKGCN) is proposed. By treating the measuring points as a graph of the total measurement points, the coupling relationship between measuring points is explicitly modeled, thus realizing the synchronous estimation of total measuring points. In addition, for the on-line monitoring scenario, a self-iteration method based on feature approximation is designed to overcome the estimation of some measurement points due to the strong coupling between measurement points under abnormal system state. The method proposed in this paper is verified on the actual data of induced draft fan in 1000-MW ultra-supercritical thermal power unit of power plant. The results show that the monitoring method proposed in this paper can detect the fault measuring points more accurately than other typical methods.
- Self-iterative feature replacement /
- multi-kernel graph convolution /
- total measurement point estimation /
- fault detection

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图 1 LSTM内部结构

Fig. 1 Internal structure of LSTM

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图 2 面向全量测点估计的多核图卷积模型结构

Fig. 2 Structure of multi-kernel graph convolution model for total measurement points estimation

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图 3 MKGCN层的计算过程

Fig. 3 Calculation process of MKGCN layer

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图 4 MKGCN层的堆叠使用

Fig. 4 The stacking use of MKGCN layers

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图 5 自迭代方法

Fig. 5 Self-iterative method

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图 6 训练数据中测点间相关性

Fig. 6 Correlation between measuring points on modeling data

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图 7 基于MKGCN的模型监测效果图($ \text{var} \in F$)

Fig. 7 Monitoring diagram of model based on MKGCN($ \text{var} \in F$)

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图 8 MEST方法漏报的部分异常变量

Fig. 8 Some abnormal variables partially missed by MEST method

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图 9 AE模型误报的部分正常变量

Fig. 9 Some normal variables with serious false alarm(AE)

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图 10 不同通道的邻接核可视化结果

Fig. 10 Visualization results of adjacency kernels in different channels

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图 11 测点12, 测点25的估计值对比

Fig. 11 Comparison of working condition estimated values of measuring point 12 and measuring point 25

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表 1 引风机测点对应表

Table 1 Measuring points of induced draft fan

0	功率信号三选值	11	引风机水平振动	22	引风机油箱温度
1	进气温度	12	引风机后轴承温度1	23	引风机中轴承温度1
2	引风机电机定子线圈温度1	13	引风机后轴承温度2	24	引风机中轴承温度2
3	引风机电机定子线圈温度2	14	引风机后轴承温度3	25	引风机中轴承温度3
4	引风机电机定子线圈温度3	15	引风机键相	26	炉膛压力
5	引风机电机水平振动1	16	引风机静叶位置反馈	27	引风机出口风温
6	引风机电机水平振动2	17	引风机前轴承温度1	28	引风机入口压力
7	引风机电机轴承温度1	18	引风机前轴承温度2	29	引风机出口风压
8	引风机电机轴承温度2	19	引风机前轴承温度3	30	引风机静叶开度指令
9	引风机电流	20	引风机润滑油温度	31	总燃料量
10	引风机风垂直振动	21	引风机润滑油压力	32	炉膛压力

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表 2 基于MKGCN层的估计模型的结构

Table 2 Structure of working condition estimation model based on MKGCN layer

序号	网络层	数目	参数	激活函数
1	BiLSTM	$n$	$[{ {\rm{input}}\_{\rm{size}}} = len, {{\rm{hidden}}\_{\rm{size}}} = ld]$	None
1	FC	$n$	$[{ {\rm{input}}\_{\rm{size}}} = len, {{\rm{output}}\_{\rm{size}}} = 2 \times ld]$	None
2	MKGCN	$1$	$[ {{c_{{\rm{in}}}} = 1,n{o_{{\rm{in}}}} = n,f{e_{{\rm{in}}}} = 2 \times ld} $	Tanh
2	MKGCN	$1$	$ {{c_{{\rm{out}}}} = oc,n{o_{{\rm{out}}}} = n,f{e_{{\rm{out}}}} = 4 \times ld}] $	Tanh
3	FC 0	$n$	$[{ {\rm{input}}\_{\rm{size}}} = 4 \times ld, {{\rm{output}}\_{\rm{size}}} = 2 \times ld]$	Tanh
4	FC 1	$n$	$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld, {{\rm{output}}\_{\rm{size}}} = 1]$	Tanh
5	FC 2	$n$	$[{ {\rm{input}}\_{\rm{size}}} = \;oc, {{\rm{output}}\_{\rm{size}}} = 1]$	None
	特征逼近层(FC)	$n$	$[{ {\rm{input}}\_{\rm{size}}} = oc, {{\rm{output}}\_{\rm{size}}} = 1]$	None

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表 3 基于GCN的估计模型结构

Table 3 Structure of working condition estimation model based on GCN

序号	网络层	数目	参数	激活函数
1	BiLSTM	$n$	$[{ {\rm{input}}\_{\rm{size}}} = len, {{\rm{hidden}}\_{\rm{size}}} = ld]$	None
2	GCN	1	$[{\rm{in}}\_{\rm{feature}} = 2 \times ld, {\rm{out}}\_{\rm{feature}} = 4 \times ld]$	Tanh
3	FC 0	$n$	$[{ {\rm{input}}\_{\rm{size}}} = 4 \times ld, {{\rm{output}}\_{\rm{size}}} = 2 \times ld]$	Tanh
4	FC 1	$n$	$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld, {{\rm{output}}\_{\rm{size}}} = ld]$	Tanh
5	FC 2	$n$	$[{ {\rm{input}}\_{\rm{size}}} = ld, {{\rm{output}}\_{\rm{size}}} = 1]$	None

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表 4 模型实现和参数网格搜索范围

Table 4 Model implementation and parameter grid search range

方法	Python包	超参数	超参数调整范围
PLSR	scikit-learn	$nc$	$nc = \left\{ {5,10,15,20,25} \right\}$
ELM	D.C. Lambert	$E,\alpha $	$ E = \left\{ {50,100,150,200,250} \right\}, $
ELM	D.C. Lambert	$E,\alpha $	$ \alpha = \left\{ {0.1,0.3,0.5,0.7,0.9} \right\} $
FC	PaddlePaddle	$ld$	$ld = \left\{ {8,16,32,64,128} \right\}$
BiLSTM	PaddlePaddle	$ld$	$ld = \left\{ {8,16,32,64,128} \right\}$
Conv1d	PaddlePaddle	$ld$	$ld = \left\{ {8,16,32,64,128} \right\}$
GCN	PaddlePaddle	$ld$	$ld = \left\{ {8,16,32,64,128} \right\}$
MKGCN	PaddlePaddle	$ld,oc$	$ ld = \left\{ {8,16,32,64,128} \right\},$
MKGCN	PaddlePaddle	$ld,oc$	$ oc = \left\{ {2,4,8,16,32} \right\} $

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表 5 网格搜索结果与深度神经网络方法在最优超参数下总参数量

Table 5 Grid search results and total parameters of depth neural network method with optimal hyperparameters

方法	最优超参数	模型数	总参数量
PLSR	$nc = 15$	$n$	$/$
ELMR	$E = 200,\alpha = 0.9$	$n$	$/$
MEST	$/$	$1$	$/$
FC	$ld = 128$	$n$	$5 \times {10^5}$
BiLSTM	$ld = 128$	$n$	$6.9 \times {10^6}$
Conv1d	$ld = 128$	$n$	$9 \times {10^5}$
GCN	$ld = 64$	$1$	$9.8 \times {10^6}$
MKGCN	$ld = 8,oc = 32$	$1$	$1.8 \times {10^5}$

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表 6 测试数据上不同模型的估计结果(RMSE)

Table 6 Results of different working condition estimation models on test data (RMSE)

变量	PLSR	ELM	FC	BiLSTM	Conv1d	GCN	MEST	MKGCN
$\text{var} \in N$	0.042	0.064	0.059	0.052	0.060	0.042	0.005	0.044
$\text{var} \in F$	0.046	0.076	0.059	0.049	0.082	0.049	0.006	0.046

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表 7 测试数据上不同模型的估计结果(MAE)

Table 7 Results of different working condition estimation models on test data (MAE)

变量	PLSR	ELM	FC	BiLSTM	Conv1d	GCN	MEST	MKGCN
$\text{var} \in N$	0.034	0.052	0.049	0.043	0.051	0.034	0.004	0.036
$\text{var} \in F$	0.039	0.066	0.050	0.041	0.070	0.043	0.005	0.039

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表 8 监测数据上各监测指标($ \text{var} \in N$)

Table 8 Monitoring indicators on monitoring data($ \text{var} \in N$)

指标	PLSR	ELM	FC	BiLSTM	Conv1d	GCN	MEST	MKGCN
$\text{False}_\text{p}$	13.267	29.573	34.267	27.392	42.581	23.568	2.853	4.500
$\text{False}_\text{n}$	0	0	0	0	0	0	0	0
F1	92.895	82.648	79.324	84.131	72.951	86.642	98.553	97.698

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表 9 监测数据上各监测指标($ \text{var} \in F$)

Table 9 Monitoring indicators on monitoring data($ \text{var} \in F$)

指标	PLSR	ELM	FC	BiLSTM	Conv1d	GCN	MEST	MKGCN
$\text{False}_\text{p}$	15.958	30.583	31.375	32.390	37.162	10.769	0	10.769
$\text{False}_\text{n}$	24.250	5.042	5.917	1.140	1.774	6.968	33.208	1.056
F1	79.681	80.203	79.362	80.302	76.644	91.092	80.090	93.836

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表 10 基于AE的估计模型的结构

Table 10 Structure of working condition estimation model based on AE

序号	网络层	数目	参数	激活函数
1	BiLSTM	1	$[{ {\rm{input}}\_{\rm{size}}} = \;len,{{\rm{hidden}}\_{\rm{size}}} = 2 \times ld]$	None
2	FC 0	1	$[{ {\rm{input}}\_{\rm{size}}} = 4 \times ld,{{\rm{output}}\_{\rm{size}}} = 2 \times ld]$	Tanh
3	FC 1	1	$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld,{{\rm{output}}\_{\rm{size}}} = ld]$	Tanh
4	FC 2	1	$[{ {\rm{input}}\_{\rm{size}}} = ld,{{\rm{output}}\_{\rm{size}}} = 2 \times ld]$	Tanh
5	FC 3	1	$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld,{{\rm{output}}\_{\rm{size}}} = n]$	None

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表 11 AE与MKGCN实验结果对比(MKGCN实验结果同表6 ~ 表9)

Table 11 Comparison of experimental results between AE and MKGCN (The experimental results of MKGCN are the same as Table 6 ~ Table 9)

指标	AE	MKGCN
RMSE, $\text{var} \in N$	0.020	0.044
RMSE, $\text{var} \in F$	0.022	0.046
MAE, $\text{var} \in N$	0.016	0.036
MAE, $\text{var} \in F$	0.019	0.039
$\text{False}_\text{p}$, $\text{var} \in N$	38.811	4.500
$\text{False}_\text{n}$, $\text{var} \in N$	0	0
F1, $\text{var} \in N$	75.922	97.698
$\text{False}_\text{p}$, $\text{var} \in F$	35.009	10.769
$\text{False}_\text{n}$, $\text{var} \in F$	0.887	1.056
F1, $\text{var} \in F$	78.505	93.836

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表 12 单输出通道与多输出通道性能对比

Table 12 Performance comparison between single output channel and multiple output channels

指标	$oc = 1$	$oc = 32$
RMSE, $\text{var} \in N$	0.043	0.044
RMSE, $\text{var} \in F$	0.055	0.046
MAE, $\text{var} \in N$	0.035	0.036
MAE, $\text{var} \in F$	0.049	0.039
$\text{False}_\text{p}$, $\text{var} \in N$	38.486	4.500
$\text{False}_\text{n}$, $\text{var} \in N$	0	0
F1, $\text{var} \in N$	76.172	97.698
$\text{False}_\text{p}$, $\text{var} \in F$	36.022	10.769
$\text{False}_\text{n}$, $\text{var} \in F$	5.237	1.056
F1, $\text{var} \in F$	76.385	93.836

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表 13 单输入通道与多输入通道性能对比

Table 13 Performance comparison between single channel and multi-channels

指标	$c_{\rm{in}}^1$	$c_{\rm{in}}^2$	$c_{\rm{in}}^{1,2}$
RMSE, $\text{var} \in N$	0.084	0.046	0.044
RMSE, $\text{var} \in F$	0.044	0.044	0.046
MAE, $\text{var} \in N$	0.072	0.038	0.036
MAE, $\text{var} \in F$	0.037	0.038	0.039
$\text{False}_\text{p}$, $\text{var} \in N$	22.703	5.527	4.500
$\text{False}_\text{n}$, $\text{var} \in N$	0	0	0
F1, $\text{var} \in N$	87.195	97.158	97.698
$\text{False}_\text{p}$, $\text{var} \in F$	33.405	15.372	10.769
$\text{False}_\text{n}$, $\text{var} \in F$	16.765	10.093	1.056
F1, $\text{var} \in F$	73.991	87.187	93.836

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表 14 自迭代效果对比

Table 14 Comparison of self iteration effect

指标	$it = 0$	$it = 5$	$it = 50$
$\text{False}_\text{p}$, $\text{var} \in N$	11.662	7.527	4.500
$\text{False}_\text{n}$, $\text{var} \in N$	0	0	0
F1, $\text{var} \in N$	93.808	96.089	97.698
$\text{False}_\text{p}$, $\text{var} \in F$	11.740	12.289	10.769
$\text{False}_\text{n}$, $\text{var} \in F$	1.732	0.676	1.055
F1, $\text{var} \in F$	93.000	93.157	93.837

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