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              面向全量測點耦合結構分析與估計的工業過程監測方法

              趙健程 趙春暉

              趙健程, 趙春暉. 面向全量測點耦合結構分析與估計的工業過程監測方法. 自動化學報, 2022, 48(x): 1?21 doi: 10.16383/j.aas.c220090
              引用本文: 趙健程, 趙春暉. 面向全量測點耦合結構分析與估計的工業過程監測方法. 自動化學報, 2022, 48(x): 1?21 doi: 10.16383/j.aas.c220090
              Zhao Jian-Cheng, Zhao Chun-Hui. An industrial process monitoring method based on total measurement point coupling structure analysis and estimation. Acta Automatica Sinica, 2022, 48(x): 1?21 doi: 10.16383/j.aas.c220090
              Citation: Zhao Jian-Cheng, Zhao Chun-Hui. An industrial process monitoring method based on total measurement point coupling structure analysis and estimation. Acta Automatica Sinica, 2022, 48(x): 1?21 doi: 10.16383/j.aas.c220090

              面向全量測點耦合結構分析與估計的工業過程監測方法

              doi: 10.16383/j.aas.c220090
              基金項目: 國家自然科學基金杰出青年基金 (62125306), 工業控制技術國家重點實驗室自主課題 (ICT2021A15), 中央高?;究蒲袠I務費專項資金資助(浙江大學NGICS大平臺)資助
              詳細信息
                作者簡介:

                趙健程:浙江大學控制科學與工程學院博士研究生. 2021年獲得浙江大學控制科學與工程學院自動化專業學士學位. 主要研究方向為大數據分析, 深度學習和零樣本學習. E-mail: zhaojiancheng@zju.edu.cn

                趙春暉:浙江大學控制科學與工程學院教授. 2003年獲得中國東北大學自動化專業學士學位, 2009年獲得中國東北大學控制理論與控制工程專業博士學位, 先后在中國香港科技大學、美國加州大學圣塔芭芭拉分校做博士后研究工作. 主要研究方向為機器學習, 工業大數據解析與應用, 包括化工, 能源以及醫療領域. 本文通信作者. E-mail: chhzhao@zju.edu.cn

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

              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: 實際工業場景中, 需要在生產過程中收集大量測點的數據, 從而掌握生產過程運行狀態. 傳統的過程監測方法通常僅評估運行狀態整體的異常與否, 或對運行狀態進行分級評估, 這種方式并不會直接定位故障部位, 不利于故障的高效檢修. 為此, 提出了一種基于全量測點估計的監測模型, 根據全量測點估計值與實際值的偏差定義監測指標, 從而實現全量測點的分別精準監測. 為了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間耦合關系建模不充分的問題, 提出了多核圖卷積網絡(Multi-kernel graph convolution network, MKGCN), 通過將全量傳感器測點視為一張全量測點圖, 顯式地對測點間耦合關系進行建模, 從而實現了全量傳感器測點的同步工況估計. 此外, 面向在線監測場景, 設計了基于特征逼近的自迭代方法, 從而克服了在異常情況下由于測點間強耦合導致的部分測點估計值異常的問題. 所提出的方法在電廠百萬千瓦超超臨界機組中引風機的實際數據上進行了驗證, 結果顯示, 提出的監測方法與其他典型方法相比能夠更精準地檢測出發生故障的測點.
              • 圖  1  LSTM內部結構

                Fig.  1  Internal structure of LSTM

                圖  2  面向全量測點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

                圖  3  MKGCN層的計算過程

                Fig.  3  Calculation process of MKGCN layer

                圖  4  MKGCN層的堆疊使用

                Fig.  4  The stacking use of MKGCN layers

                圖  5  自迭代方法

                Fig.  5  Self-iterative method

                圖  6  訓練數據中測點間相關性

                Fig.  6  Correlation between measuring points on modeling data

                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$)

                圖  8  MEST方法漏報的部分異常變量

                Fig.  8  Some abnormal variables partially missed by MEST method

                圖  9  AE模型誤報的部分正常變量

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

                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

                圖  11  測點12, 測點25的估計值對比

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

                表  1  引風機測點對應表

                Table  1  Measuring points of induced draft fan

                0功率信號三選值11引風機水平振動22引風機油箱溫度
                1進氣溫度12引風機后軸承溫度123引風機中軸承溫度1
                2引風機電機定子線圈溫度113引風機后軸承溫度224引風機中軸承溫度2
                3引風機電機定子線圈溫度214引風機后軸承溫度325引風機中軸承溫度3
                4引風機電機定子線圈溫度315引風機鍵相26爐膛壓力
                5引風機電機水平振動116引風機靜葉位置反饋27引風機出口風溫
                6引風機電機水平振動217引風機前軸承溫度128引風機入口壓力
                7引風機電機軸承溫度118引風機前軸承溫度229引風機出口風壓
                8引風機電機軸承溫度219引風機前軸承溫度330引風機靜葉開度指令
                9引風機電流20引風機潤滑油溫度31總燃料量
                10引風機風垂直振動21引風機潤滑油壓力32爐膛壓力
                下載: 導出CSV

                表  2  基于MKGCN層的估計模型的結構

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

                序號網絡層數目參數激活函數
                1BiLSTM$n$$[{ {\rm{input}}\_{\rm{size}}} = len, {{\rm{hidden}}\_{\rm{size}}} = ld]$None
                FC$n$$[{ {\rm{input}}\_{\rm{size}}} = len, {{\rm{output}}\_{\rm{size}}} = 2 \times ld]$
                2MKGCN$1$$[ {{c_{{\rm{in}}}} = 1,n{o_{{\rm{in}}}} = n,f{e_{{\rm{in}}}} = 2 \times ld} $Tanh
                $ {{c_{{\rm{out}}}} = oc,n{o_{{\rm{out}}}} = n,f{e_{{\rm{out}}}} = 4 \times ld}] $
                3FC 0$n$$[{ {\rm{input}}\_{\rm{size}}} = 4 \times ld, {{\rm{output}}\_{\rm{size}}} = 2 \times ld]$Tanh
                4FC 1$n$$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld, {{\rm{output}}\_{\rm{size}}} = 1]$Tanh
                5FC 2$n$$[{ {\rm{input}}\_{\rm{size}}} = \;oc, {{\rm{output}}\_{\rm{size}}} = 1]$None
                特征逼近層(FC)$n$$[{ {\rm{input}}\_{\rm{size}}} = oc, {{\rm{output}}\_{\rm{size}}} = 1]$None
                下載: 導出CSV

                表  3  基于GCN的估計模型結構

                Table  3  Structure of working condition estimation model based on GCN

                序號網絡層數目參數激活函數
                1BiLSTM$n$$[{ {\rm{input}}\_{\rm{size}}} = len, {{\rm{hidden}}\_{\rm{size}}} = ld]$None
                2GCN1$[{\rm{in}}\_{\rm{feature}} = 2 \times ld, {\rm{out}}\_{\rm{feature}} = 4 \times ld]$Tanh
                3FC 0$n$$[{ {\rm{input}}\_{\rm{size}}} = 4 \times ld, {{\rm{output}}\_{\rm{size}}} = 2 \times ld]$Tanh
                4FC 1$n$$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld, {{\rm{output}}\_{\rm{size}}} = ld]$Tanh
                5FC 2$n$$[{ {\rm{input}}\_{\rm{size}}} = ld, {{\rm{output}}\_{\rm{size}}} = 1]$None
                下載: 導出CSV

                表  4  模型實現和參數網格搜索范圍

                Table  4  Model implementation and parameter grid search range

                方法Python包超參數超參數調整范圍
                PLSRscikit-learn$nc$$nc = \left\{ {5,10,15,20,25} \right\}$
                ELMD.C. Lambert$E,\alpha $$ E = \left\{ {50,100,150,200,250} \right\}, $
                $ \alpha = \left\{ {0.1,0.3,0.5,0.7,0.9} \right\} $
                FCPaddlePaddle$ld$$ld = \left\{ {8,16,32,64,128} \right\}$
                BiLSTMPaddlePaddle$ld$$ld = \left\{ {8,16,32,64,128} \right\}$
                Conv1dPaddlePaddle$ld$$ld = \left\{ {8,16,32,64,128} \right\}$
                GCNPaddlePaddle$ld$$ld = \left\{ {8,16,32,64,128} \right\}$
                MKGCNPaddlePaddle$ld,oc$$ ld = \left\{ {8,16,32,64,128} \right\},$
                $ oc = \left\{ {2,4,8,16,32} \right\} $
                下載: 導出CSV

                表  5  網格搜索結果與深度神經網絡方法在最優超參數下總參數量

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

                方法最優超參數模型數總參數量
                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}$
                下載: 導出CSV

                表  6  測試數據上不同模型的估計結果(RMSE)

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

                變量PLSRELMFCBiLSTMConv1dGCNMESTMKGCN
                $\text{var} \in N$0.0420.0640.0590.0520.0600.0420.0050.044
                $\text{var} \in F$0.0460.0760.0590.0490.0820.0490.0060.046
                下載: 導出CSV

                表  7  測試數據上不同模型的估計結果(MAE)

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

                變量PLSRELMFCBiLSTMConv1dGCNMESTMKGCN
                $\text{var} \in N$0.0340.0520.0490.0430.0510.0340.0040.036
                $\text{var} \in F$0.0390.0660.0500.0410.0700.0430.0050.039
                下載: 導出CSV

                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PLSRELMFCBiLSTMConv1dGCNMESTMKGCN
                $\text{False}_\text{p}$13.26729.57334.26727.39242.58123.5682.8534.500
                $\text{False}_\text{n}$00000000
                F192.89582.64879.32484.13172.95186.64298.55397.698
                下載: 導出CSV

                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PLSRELMFCBiLSTMConv1dGCNMESTMKGCN
                $\text{False}_\text{p}$15.95830.58331.37532.39037.16210.769010.769
                $\text{False}_\text{n}$24.2505.0425.9171.1401.7746.96833.2081.056
                F179.68180.20379.36280.30276.64491.09280.09093.836
                下載: 導出CSV

                表  10  基于AE的估計模型的結構

                Table  10  Structure of working condition estimation model based on AE

                序號網絡層數目參數激活函數
                1BiLSTM1$[{ {\rm{input}}\_{\rm{size}}} = \;len,{{\rm{hidden}}\_{\rm{size}}} = 2 \times ld]$None
                2FC 01$[{ {\rm{input}}\_{\rm{size}}} = 4 \times ld,{{\rm{output}}\_{\rm{size}}} = 2 \times ld]$Tanh
                3FC 11$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld,{{\rm{output}}\_{\rm{size}}} = ld]$Tanh
                4FC 21$[{ {\rm{input}}\_{\rm{size}}} = ld,{{\rm{output}}\_{\rm{size}}} = 2 \times ld]$Tanh
                5FC 31$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld,{{\rm{output}}\_{\rm{size}}} = n]$None
                下載: 導出CSV

                表  11  AE與MKGCN實驗結果對比(MKGCN實驗結果同表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)

                指標AEMKGCN
                RMSE, $\text{var} \in N$0.0200.044
                RMSE, $\text{var} \in F$0.0220.046
                MAE, $\text{var} \in N$0.0160.036
                MAE, $\text{var} \in F$0.0190.039
                $\text{False}_\text{p}$, $\text{var} \in N$38.8114.500
                $\text{False}_\text{n}$, $\text{var} \in N$00
                F1, $\text{var} \in N$75.92297.698
                $\text{False}_\text{p}$, $\text{var} \in F$35.00910.769
                $\text{False}_\text{n}$, $\text{var} \in F$0.8871.056
                F1, $\text{var} \in F$78.50593.836
                下載: 導出CSV

                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$0.0430.044
                RMSE, $\text{var} \in F$0.0550.046
                MAE, $\text{var} \in N$0.0350.036
                MAE, $\text{var} \in F$0.0490.039
                $\text{False}_\text{p}$, $\text{var} \in N$38.4864.500
                $\text{False}_\text{n}$, $\text{var} \in N$00
                F1, $\text{var} \in N$76.17297.698
                $\text{False}_\text{p}$, $\text{var} \in F$36.02210.769
                $\text{False}_\text{n}$, $\text{var} \in F$5.2371.056
                F1, $\text{var} \in F$76.38593.836
                下載: 導出CSV

                表  13  單輸入通道與多輸入通道性能對比

                Table  13  Performance comparison between single channel and multi-channels

                指標$c_{\rm{in}}^1$$c_{\rm{in}}^2$$c_{\rm{in}}^{1,2}$
                RMSE, $\text{var} \in N$0.0840.0460.044
                RMSE, $\text{var} \in F$0.0440.0440.046
                MAE, $\text{var} \in N$0.0720.0380.036
                MAE, $\text{var} \in F$0.0370.0380.039
                $\text{False}_\text{p}$, $\text{var} \in N$22.7035.5274.500
                $\text{False}_\text{n}$, $\text{var} \in N$000
                F1, $\text{var} \in N$87.19597.15897.698
                $\text{False}_\text{p}$, $\text{var} \in F$33.40515.37210.769
                $\text{False}_\text{n}$, $\text{var} \in F$16.76510.0931.056
                F1, $\text{var} \in F$73.99187.18793.836
                下載: 導出CSV

                表  14  自迭代效果對比

                Table  14  Comparison of self iteration effect

                指標$it = 0$$it = 5$$it = 50$
                $\text{False}_\text{p}$, $\text{var} \in N$11.6627.5274.500
                $\text{False}_\text{n}$, $\text{var} \in N$000
                F1, $\text{var} \in N$93.80896.08997.698
                $\text{False}_\text{p}$, $\text{var} \in F$11.74012.28910.769
                $\text{False}_\text{n}$, $\text{var} \in F$1.7320.6761.055
                F1, $\text{var} \in F$93.00093.15793.837
                下載: 導出CSV
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                      1. [1] 柴天佑. 工業人工智能發展方向. 自動化學報, 2020, 46(10): 2003-2012

                        Chai Tian-You. Development directions of industrial artificial intelligence. Acta Automatica Sinica, 2020, 46(10): 2003-2012
                        [2] 馬亮, 彭開香, 董潔. 工業過程故障根源診斷與傳播路徑識別技術綜述. 自動化學報, 2022, 48(7): 1650-1663

                        Ma Liang, Peng Kai-Xiang, Dong Jie. Review of root cause diagnosis and propagation path identification techniques for faults in industrial processes. Acta Automatica Sinica, 2022, 48(7): 1650-1663
                        [3] Zhao C H. Perspectives on nonstationary process monitoring in the era of industrial artificial intelligence. Journal of Process Control, 2022, 116: 255-272 doi: 10.1016/j.jprocont.2022.06.011
                        [4] He Y L, Geng Z Q, Zhu Q X. Soft sensor development for the key variables of complex chemical processes using a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square. Chemometrics and Intelligent Laboratory Systems, 2016, 151: 78-88 doi: 10.1016/j.chemolab.2015.12.010
                        [5] 趙春暉, 胡赟昀, 鄭嘉樂, 陳軍豪. 數據驅動的燃煤發電裝備運行工況監控——現狀與展望. 自動化學報, (未出版)

                        Zhao Chun-Hui, Hu Yun-Yun, Zheng Jia-Le, Chen Jun-Hao. Data-driven operating monitoring for coal-fired power generation equipment: The state of the art and challenge. Acta Automatica Sinica, to be published
                        [6] Sun X, Marquez H J, Chen T W, Riaz M. An improved PCA method with application to boiler leak detection. ISA Transactions, 2005, 44(3): 379-397 doi: 10.1016/S0019-0578(07)60211-0
                        [7] You L X, Chen J. A variable relevant multi-local PCA modeling scheme to monitor a nonlinear chemical process. Chemical Engineering Science, 2021, 246: Article No. 116851 doi: 10.1016/j.ces.2021.116851
                        [8] Zhao C H, Sun H. Dynamic distributed monitoring strategy for large-scale nonstationary processes subject to frequently varying conditions under closed-loop control. IEEE Transactions on Industrial Electronics, 2019, 66(6): 4749-4758 doi: 10.1109/TIE.2018.2864703
                        [9] Song P Y, Zhao C H. Slow down to go better: A survey on slow feature analysis. IEEE Transactions on Neural Networks and Learning Systems, to be published
                        [10] Zhao C H, Chen J H, Jing H. Condition-driven data analytics and monitoring for wide-range nonstationary and transient continuous processes. IEEE Transactions on Automation Science and Engineering, 2021, 18(4): 1563-1574 doi: 10.1109/TASE.2020.3010536
                        [11] 樊繼聰, 王友清, 秦泗釗. 聯合指標獨立成分分析在多變量過程故障診斷中的應用. 自動化學報, 2013, 39(5): 494-501

                        Fan Ji-Cong, Wang You-Qing, Qin Si-Zhao. Combined indices for ICA and their applications to multivariate process fault diagnosis. Acta Automatica Sinica, 2013, 39(5): 494-501
                        [12] Ma L Y, Ma Y G, Lee K Y. An intelligent power plant fault diagnostics for varying degree of severity and loading conditions. IEEE Transactions on Energy Conversion, 2010, 25(2): 546-554 doi: 10.1109/TEC.2009.2037435
                        [13] Zhao R, Yan R Q, Wang J J, Mao K Z. Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors, 2017, 17(2): Article No. 273 doi: 10.3390/s17020273
                        [14] Shen Y, Abubakar M, Liu H, Hussain F. Power quality disturbance monitoring and classification based on improved PCA and convolution neural network for wind-grid distribution systems. Energies, 2019, 12(7): Article No. 1280 doi: 10.3390/en12071280
                        [15] Yu J, Rashid M M. A novel dynamic Bayesian network-based networked process monitoring approach for fault detection, propagation identification, and root cause diagnosis. AIChE Journal, 2013, 59(7): 2348-2365 doi: 10.1002/aic.14013
                        [16] Dimokranitou A. Adversarial Autoencoders for Anomalous Event Detection in Images[Master dissertation], Purdue University, USA, 2017.
                        [17] De Castro-Cros M, Rosso S, Bahilo E, Velasco M, Angulo C. Condition assessment of industrial gas turbine compressor using a drift soft sensor based in autoencoder. Sensors, 2021, 21(8): Article No. 2708 doi: 10.3390/s21082708
                        [18] Lutz M A, Vogt S, Berkhout V, Faulstich S, Dienst S, Steinmetz U, et al. Evaluation of anomaly detection of an autoencoder based on maintenace information and scada-data. Energies, 2020, 13(5): Article No. 1063 doi: 10.3390/en13051063
                        [19] Guo Y F, Liao W X, Wang Q L, Yu L X, Ji T X, Li P. Multidimensional time series anomaly detection: A GRU-based Gaussian mixture variational autoencoder approach. In: Proceedings of the 10th Asian Conference on Machine Learning. Beijing, China: PMLR, 2018. 97?112 (查閱所有網上資料, 出版地信息不確定, 請核對)
                        [20] Yu W K, Zhao C H. Robust monitoring and fault isolation of nonlinear industrial processes using denoising autoencoder and elastic net. IEEE Transactions on Control Systems Technology, 2020, 28(3): 1083-1091 doi: 10.1109/TCST.2019.2897946
                        [21] Hu Y Y, Wang Y, Zhao C H. A sparse fault degradation oriented fisher discriminant analysis (FDFDA) algorithm for faulty variable isolation and its industrial application. Control Engineering Practice, 2019, 90: 311-320 doi: 10.1016/j.conengprac.2019.07.007
                        [22] 趙春暉, 余萬科, 高福榮. 非平穩間歇過程數據解析與狀態監控——回顧與展望. 自動化學報, 2020, 46(10): 2072-2091 doi: 10.16383/j.aas.c190586

                        Zhao Chun-Hui, Yu Wan-Ke, Gao Fu-Rong. Data analytics and condition monitoring methods for nonstationary batch processes-Current status and future. Acta Automatica Sinica, 2020, 46(10): 2072-2091 doi: 10.16383/j.aas.c190586
                        [23] Gross K C, Singer R M, Wegerich S W, Herzog J P, VanAlstine R, Bockhorst F. Application of a model-based fault detection system to nuclear plant signals. In: Proceedings of the 9th International Conference on Intelligent Systems Applications to Power Systems. Seoul, Korea: Argonne National Lab., 1997.
                        [24] Zavaljevski N, Gross K C. Sensor fault detection in nuclear power plants using multivariate state estimation technique and support vector machines. In: Proceedings of the 3rd International Conference of the Yugoslav Nuclear Society. Belgrade, Yugoslavia: Argonne National Lab., 2020.
                        [25] Cheng S F, Pecht M. Multivariate state estimation technique for remaining useful life prediction of electronic products. In: Proceedings of the 2007 AAAI Fall Symposium on Artificial Intelligence for Prognostics. Arlington, USA: AAAI, 2007.
                        [26] Wang Z Q, Liu C L. Wind turbine condition monitoring based on a novel multivariate state estimation technique. Measurement, 2021, 168: Article No. 108388 doi: 10.1016/j.measurement.2020.108388
                        [27] Bockhorst F K, Gross K C, Herzog J P, Wegerich S W. MSET modeling of crystal river-3 venturi flow meters. In: Proceedings of the 6th International Conference on Nuclear Engineering. San Diego, USA: Argonne National Lab., 1998.
                        [28] Fan Y J, Tao B, Zheng Y, Jang S S. A data-driven soft sensor based on multilayer perceptron neural network with a double LASSO approach. IEEE Transactions on Instrumentation and Measurement, 2020, 69(7): 3972-3979 doi: 10.1109/TIM.2019.2947126
                        [29] Zhang M, Liu X G, Zhang Z Y. A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine. Chinese Journal of Chemical Engineering, 2016, 24(8): 1013-1019 doi: 10.1016/j.cjche.2016.05.030
                        [30] Ke W S, Huang D X, Yang F, Jiang Y H. Soft sensor development and applications based on LSTM in deep neural networks. In: Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Honolulu, USA: IEEE, 2017. 1?6
                        [31] Yuan X F, Qi S B, Wang Y L, Xia H B. A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data. Control Engineering Practice, 2020, 104: Article No. 104614 doi: 10.1016/j.conengprac.2020.104614
                        [32] Zhu W B, Ma Y, Zhou Y Z, Benton M, Romagnoli J. Deep learning based soft sensor and its application on a pyrolysis reactor for compositions predictions of gas phase components. Computer Aided Chemical Engineering, 2018, 44: 2245-2250
                        [33] 常樹超, 趙春暉. 一種時空協同的圖卷積長短期記憶網絡及其工業軟測量應用. 控制與決策, 2022, 37(1): 77-86

                        Chang Shu-Chao, Zhao Chun-Hui. A spatio-temporal synergistic graph convolution long short-term memory network and its application for industrial soft sensors. Control and Decision, 2022, 37(1): 77-86
                        [34] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. arXiv: 1609.02907, 2016.(查閱所有網上資料, 不確定文獻類型及格式是否正確, 請核對)
                        [35] Feng L J, Zhao C H, Li Y L, Zhou M, Qiao H L, Fu C. Multichannel diffusion graph convolutional network for the prediction of endpoint composition in the converter steelmaking process. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-13
                        [36] Wu Z H, Pan S R, Long G D, Jiang J, Chang X J, Zhang C Q. Connecting the dots: Multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: Association for Computing Machinery, 2020. 753?763(查閱所有網上資料, 出版地信息不確定, 請核對)
                        [37] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735-1780 doi: 10.1162/neco.1997.9.8.1735
                        [38] Gers F A, Schmidhuber J, Cummins F. Learning to forget: Continual prediction with LSTM. Neural Computation, 2000, 12(10): 2451-2471 doi: 10.1162/089976600300015015
                        [39] Feng L J, Zhao C H, Sun Y X. Dual attention-based encoder-decoder: A customized sequence-to-sequence learning for soft sensor development. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(8): 3306-3317 doi: 10.1109/TNNLS.2020.3015929
                        [40] Feng L J, Zhao C H, Huang B. Adversarial smoothing tri-regression for robust semi-supervised industrial soft sensor. Journal of Process Control, 2021, 108: 86-97 doi: 10.1016/j.jprocont.2021.11.001
                        [41] Schuster M, Paliwal K K. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 1997, 45(11): 2673-2681 doi: 10.1109/78.650093
                        [42] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60(6): 84-90 doi: 10.1145/3065386
                        [43] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, et al. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: ACM, 2017. 6000?6010
                        [44] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. Sardinia, Italy: PMLR, 2010. 249?256
                        [45] Li Q M, Han Z C, Wu X M. Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 30th Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. New Orleans, USA: AAAI, 2018. 3538?3545
                        [46] Chiang W L, Liu X Q, Si S, Li Y, Bengio S, Hsieh C J. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage, USA: Association for Computing Machinery, 2019. 257?266
                        [47] Terrell G R, Scott D W. Variable kernel density estimation. The Annals of Statistics, 1992, 20(3): 1236-1265
                        [48] Gilbertson D D, Kent M, Pyatt F B. Data analysis and interpretation III: Correlation and regression using spearman’s rank correlation coefficient and semi-averages regression. Practical Ecology for Geography and Biology. New York, USA: Springer, 1985. 218?236
                        [49] Geladi P, Kowalski B R. Partial least-squares regression: A tutorial. Analytica Chimica Acta, 1986, 185: 1-17 doi: 10.1016/0003-2670(86)80028-9
                        [50] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1-3): 489-501 doi: 10.1016/j.neucom.2005.12.126
                        [51] Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman D J. 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing, 2021, 151: Article No. 107398 doi: 10.1016/j.ymssp.2020.107398
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