面向全量測點(diǎn)耦合結構分析與估計的工業(yè)過(guò)程監測方法

趙健程; 趙春暉

doi:10.16383/j.aas.c220090

面向全量測點(diǎn)耦合結構分析與估計的工業(yè)過(guò)程監測方法

doi: 10.16383/j.aas.c220090

趙健程^1,,
趙春暉^1,

1.
浙江大學(xué)控制科學(xué)與工程學(xué)院杭州 310027

基金項目: 國家自然科學(xué)基金杰出青年基金 (62125306), 國家自然科學(xué)基金重點(diǎn)項目 (62133003)資助

詳細信息

作者簡(jiǎn)介:
趙健程：浙江大學(xué)控制科學(xué)與工程學(xué)院博士研究生. 2021年獲得浙江大學(xué)學(xué)士學(xué)位. 主要研究方向為大數據分析, 深度學(xué)習和零樣本學(xué)習. E-mail: zhaojiancheng@zju.edu.cn

趙春暉：浙江大學(xué)控制科學(xué)與工程學(xué)院教授. 2003年, 2006年, 2009年分別獲得東北大學(xué)學(xué)士、碩士和博士學(xué)位. 主要研究方向為機器學(xué)習, 工業(yè)大數據解析與應用, 包括化工, 能源以及醫療領(lǐng)域. 本文通信作者. E-mail: chhzhao@zju.edu.cn

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出版歷程
- 收稿日期: 2022-02-07
- 錄用日期: 2022-09-06
- 網(wǎng)絡(luò )出版日期: 2022-10-08
- 刊出日期: 2024-08-22

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

ZHAO Jian-Cheng^1
,,
ZHAO Chun-Hui^1
,

1.
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027

Funds: Supported by National Natural Science Foundation of China for Distinguished Young Scholars (62125306) and National Nat-ural Science Foundation of China (62133003)

More Information

Author Bio:
ZHAO Jian-Cheng　Ph.D. candidate at the College of Control Science and Engineering, Zhejiang University. He received his bachelor degree from 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 Northeastern University, in 2003, 2006, and 2009, respectively. Her research interest covers machine learning, analytics of industrial big data, and their applications in chemical, energy, and medical fields. Corresponding author of this paper

摘要

摘要: 實(shí)際工業(yè)場(chǎng)景中, 需要在生產(chǎn)過(guò)程中收集大量測點(diǎn)的數據, 從而掌握生產(chǎn)過(guò)程運行狀態(tài). 傳統的過(guò)程監測方法通常僅評估運行狀態(tài)整體的異常與否, 或對運行狀態(tài)進(jìn)行分級評估, 這種方式并不會(huì )直接定位故障部位, 不利于故障的高效檢修. 為此, 提出一種基于全量測點(diǎn)估計的監測模型, 根據全量測點(diǎn)估計值與實(shí)際值的偏差定義監測指標, 從而實(shí)現全量測點(diǎn)的分別精準監測. 為克服原有的基于工況估計的監測方法監測不全面且對測點(diǎn)間耦合關(guān)系建模不充分的問(wèn)題, 提出多核圖卷積網(wǎng)絡(luò )(Multi-kernel graph convolutional network, MKGCN), 通過(guò)將全量傳感器測點(diǎn)視為一張全量測點(diǎn)圖, 顯式地對測點(diǎn)間耦合關(guān)系進(jìn)行建模, 從而實(shí)現全量傳感器測點(diǎn)的同步工況估計. 此外, 面向在線(xiàn)監測場(chǎng)景, 設計基于特征逼近的自迭代方法, 從而克服在異常情況下由于測點(diǎn)間強耦合導致的部分測點(diǎn)估計值異常的問(wèn)題. 所提出的方法在電廠(chǎng)百萬(wàn)千瓦超超臨界機組中引風(fēng)機的實(shí)際數據上進(jìn)行驗證, 結果顯示, 與其他典型方法相比, 所提出的監測方法能夠更精準地檢測出發(fā)生故障的測點(diǎn).
- 自迭代特征替換 /
- 多核圖卷積網(wǎng)絡(luò ) /
- 全量測點(diǎn)估計 /
- 故障檢測
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 operational 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 do not 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 issue of abnormal 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 convolutional network (MKGCN) /
- total measurement point estimation /
- fault detection

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圖 1 LSTM內部結構

Fig. 1 Internal structure of LSTM

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圖 2 面向全量測點(diǎn)估計的多核圖卷積模型結構

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

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圖 3 MKGCN層的計算過(guò)程

Fig. 3 Calculation process of MKGCN layer

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圖 4 MKGCN層的堆疊使用

Fig. 4 The stacking use of MKGCN layers

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圖 5 自迭代方法

Fig. 5 Self-iterative method

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圖 6 訓練數據中測點(diǎn)間相關(guān)性

Fig. 6 Correlation between measuring points on training data

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圖 7 基于MKGCN的模型監測效果圖$( \text{var} \in F)$

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

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圖 8 MEST方法漏報的部分異常變量

Fig. 8 Some abnormal variables partially missed by MEST method

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圖 9 AE模型誤報的部分正常變量

Fig. 9 Some normal variables with serious false alarm by AE model

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圖 10 不同通道的鄰接核可視化結果

Fig. 10 Visualization results of adjacency kernels in different channels

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圖 11 測點(diǎn)12和測點(diǎn)25的工況估計值對比

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

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表 1 引風(fēng)機測點(diǎn)對應表

Table 1 Measuring points of induced draft fan

測點(diǎn)編號	物理量	測點(diǎn)編號	物理量	測點(diǎn)編號	物理量
0	功率信號三選值	11	引風(fēng)機水平振動(dòng)	22	引風(fēng)機油箱溫度
1	進(jìn)氣溫度	12	引風(fēng)機后軸承溫度 1	23	引風(fēng)機中軸承溫度 1
2	引風(fēng)機電機定子線(xiàn)圈溫度 1	13	引風(fēng)機后軸承溫度 2	24	引風(fēng)機中軸承溫度 2
3	引風(fēng)機電機定子線(xiàn)圈溫度 2	14	引風(fēng)機后軸承溫度 3	25	引風(fēng)機中軸承溫度 3
4	引風(fēng)機電機定子線(xiàn)圈溫度 3	15	引風(fēng)機鍵相	26	爐膛壓力
5	引風(fēng)機電機水平振動(dòng) 1	16	引風(fēng)機靜葉位置反饋	27	引風(fēng)機出口風(fēng)溫
6	引風(fēng)機電機水平振動(dòng) 2	17	引風(fēng)機前軸承溫度 1	28	引風(fēng)機入口壓力
7	引風(fēng)機電機軸承溫度 1	18	引風(fēng)機前軸承溫度 2	29	引風(fēng)機出口風(fēng)壓
8	引風(fēng)機電機軸承溫度 2	19	引風(fēng)機前軸承溫度 3	30	引風(fēng)機靜葉開(kāi)度指令
9	引風(fēng)機電流	20	引風(fēng)機潤滑油溫度	31	總燃料量
10	引風(fēng)機風(fēng)垂直振動(dòng)	21	引風(fēng)機潤滑油壓力	32	爐膛壓力

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表 2 基于MKGCN層的工況估計模型結構

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

序號	網(wǎng)絡(luò )層	數目	參數	激活函數
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} $, $ {{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
6	特征逼近層 (FC)	$n$	$[{ {\rm{input}}\_{\rm{size}}} = oc, {{\rm{output}}\_{\rm{size}}} = 1]$	None

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表 3 基于GCN的工況估計模型結構

Table 3 Structure of working condition estimation model based on GCN

序號	網(wǎng)絡(luò )層	數目	參數	激活函數
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 模型實(shí)現和參數網(wǎng)格搜索范圍

Table 4 Model implementation and parameter grid search range

方法	Python包	超參數	超參數調整范圍
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\}, $ $ \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\},$ $ oc = \left\{ {2,4,8,16,32} \right\} $

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表 5 網(wǎng)格搜索結果與深度神經(jīng)網(wǎng)絡(luò )方法在最優(yōu)超參數下總參數量

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

方法	最優(yōu)超參數	模型數	總參數量
PLSR	$nc = 15$	$n$	/
ELM	$E = 200,\alpha = 0.9$	$n$	/
MEST	/	/	/
FC	$ld = 128$	$n$	5 × 10⁵
BiLSTM	$ld = 128$	$n$	6.9 × 10⁶
Conv1D	$ld = 128$	$n$	9 × 10⁵
GCN	$ld = 64$	$1$	9.8 × 10⁶
MKGCN	$ld = 8,oc = 32$	$1$	1.8 × 10⁵

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表 6 測試數據上不同模型的工況估計結果(RMSE)

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

變量	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 測試數據上不同模型的工況估計結果(MAE)

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

變量	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 監測數據上各監測指標$( \text{var} \in N)$

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

指標	PLSR	ELM	FC	BiLSTM	Conv1D	GCN	MEST	MKGCN
${False}_\text{p}$	13.267	29.573	34.267	27.392	42.581	23.568	2.853	4.500
${False}_\text{n}$	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 監測數據上各監測指標$( \text{var} \in F)$

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

指標	PLSR	ELM	FC	BiLSTM	Conv1D	GCN	MEST	MKGCN
${False}_\text{p}$	15.958	30.583	31.375	32.390	37.162	10.769	0	10.769
${False}_\text{n}$	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A(yíng)E的工況估計模型的結構

Table 10 Structure of working condition estimation model based on AE

序號	網(wǎng)絡(luò )層	數目	參數	激活函數
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與MKGCN實(shí)驗結果對比(MKGCN實(shí)驗結果同表6 ~ 9)

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

指標	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
${False}_\text{p}$, $\text{var} \in N$	38.811	4.500
${False}_\text{n}$, $\text{var} \in N$	0	0
F1, $\text{var} \in N$	75.922	97.698
${False}_\text{p}$, $\text{var} \in F$	35.009	10.769
${False}_\text{n}$, $\text{var} \in F$	0.887	1.056
F1, $\text{var} \in F$	78.505	93.836

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表 12 單輸出通道與多輸出通道性能對比

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

指標	$oc = 1$	$oc = 32$
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
${False}_\text{p}$, $\text{var} \in N$	38.486	4.500
${False}_\text{n}$, $\text{var} \in N$	0	0
F1, $\text{var} \in N$	76.172	97.698
${False}_\text{p}$, $\text{var} \in F$	36.022	10.769
${False}_\text{n}$, $\text{var} \in F$	5.237	1.056
F1, $\text{var} \in F$	76.385	93.836

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表 13 單輸入通道與多輸入通道性能對比

Table 13 Performance comparison between single input channel and multiple input channels

指標	$c_{\rm{in}}^1$	$c_{\rm{in}}^2$	$c_{\rm{in}}^{1,2}$
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
${False}_\text{p}$, $\text{var} \in N$	22.703	5.527	4.500
${False}_\text{n}$, $\text{var} \in N$	0	0	0
F1, $\text{var} \in N$	87.195	97.158	97.698
${False}_\text{p}$, $\text{var} \in F$	33.405	15.372	10.769
${False}_\text{n}$, $\text{var} \in F$	16.765	10.093	1.056
F1, $\text{var} \in F$	73.991	87.187	93.836

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表 14 自迭代效果對比

Table 14 Comparison of self-iteration effect

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

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