2.793

                    2018影響因子

                    (CJCR)

                    • 中文核心
                    • EI
                    • 中國科技核心
                    • Scopus
                    • CSCD
                    • 英國科學文摘

                    留言板

                    尊敬的讀者、作者、審稿人, 關于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復。謝謝您的支持!

                    姓名
                    郵箱
                    手機號碼
                    標題
                    留言內容
                    驗證碼

                    非平穩間歇過程數據解析與狀態監控回顧與展望

                    趙春暉 余萬科 高福榮

                    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
                    引用本文: 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
                    Citation: 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

                    非平穩間歇過程數據解析與狀態監控回顧與展望

                    doi: 10.16383/j.aas.c190586
                    基金項目: NSFC-浙江省兩化融合基金(U1709211), 浙江省重點研發計劃項目(2019C03100), 浙江省重點研發計劃項目(2019C01048)資助
                    詳細信息
                      作者簡介:

                      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

                      余萬科:浙江大學控制科學與工程學院博士研究生. 2016年獲得北京航空航天大學宇航學院碩士學位, 2013年獲得東北大學數學系學士學位. 主要研究方向為故障診斷, 過程監測. E-mail: yuwanke@zju.edu.cn

                      高福榮:中國香港科技大學化學與生物分子工程學系講座教授. 1985 年獲得中國石油大學自動化專業學士學位, 1989 年和1993 年在加拿大麥吉爾大學獲得碩士和博士學位. 主要研究方向為過程檢測與故障診斷, 批次過程控制, 高分子材料加工及優化. E-mail: kefgao@ust.hk

                    Data Analytics and Condition Monitoring Methods for Nonstationary Batch Processes — Current Status and Future

                    Funds: Supported by NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization (U1709211), Zhejiang Key Research and Development Project (2019C03100), and Zhejiang Key Research and Development Project (2019C01048)
                    • 摘要: 間歇過程作為制造業的重要生產方式之一, 其高效運行是智能制造的優先主題. 為了保障生產過程的高效運行, 面向間歇生產的過程數據解析與狀態監控算法在最近三十年間得到大家的廣泛關注, 發展速度穩步提升. 但由于間歇過程本身的多重時變大范圍非平穩運行復雜特性, 以及對狀態監控與故障診斷要求的提高, 現有的理論和方法仍面臨著挑戰. 本文從分析間歇過程的特性出發, 從數據解析的角度, 總結了近三十年來非平穩間歇過程高性能監控研究的發展. 一方面對間歇過程監控領域幾種經典的方法體系進行了總結和梳理, 另一方面揭示了尚存在的問題以及未來可能的研究思路和發展脈絡.
                    • 圖  1  “多重時變”本質特性示意圖

                      Fig.  1  The characteristics of the batch process

                      圖  2  間歇過程的三維數據表示[16]

                      Fig.  2  Batch process data in three dimensions[16]

                      圖  3  將三維數據展開成二維數據的6種方式

                      Fig.  3  Unfold the three dimensions data into two dimensions using six different manners

                      圖  4  不等長操作時段的間歇過程示意圖

                      Fig.  4  An example of the batch process with uneven-length batches

                      圖  5  間歇過程多模態切換示意圖

                      Fig.  5  Normal shift of operation phases in batch process

                      圖  6  兩模態間歇過程時段分析結果

                      Fig.  6  Analysis result of batch process with two operation phases

                      表  1  時段劃分方法總結對比

                      Table  1  The comparison of different phase partition methods

                      時段劃分方法 劃分依據 優點 缺點
                      過程機理法[45, 48, 72] 利用實際間歇工業過程運行機理的變化來劃分過程運行時段, 要求一定的專家經驗和過程知識. 如果間歇生產過程相對簡單或者工程師對此比較熟悉, 則可以比較容易地獲取過程機理知識實現時段劃分. 工業生產過程往往機理復雜, 很難在短時間內獲取相關的知識和經驗, 從而極大地限制和約束了其順利實施施和推廣應用.
                      特征分析方法[7375] 時段的切換對應引起相應測量變量的變化. 對某些過程變量或從中提取的特征變量進行分析, 借助其沿時間軸上的變化判斷時段信息. 指示變量方法是其中一種典型代表. 當時段發生切換或者變化, 過程特性變化, 相應的某些過程變量或是特征變量亦發生顯著變化, 可用于指示不同時段. 算法較為簡單. 并不是每個工業過程中都存在并能找到這樣的“指示”變量.
                      k-means[6266] 通過相似度度量, 分析不同時間點上的潛在相關特性的相似與不同, 如果時間片具有相似特性則被歸到同一類中, 具有顯著差異則被分到不同類中. 該方法能夠自動劃分不同的多個時段, 不需借助任何過程機理和知識. 分類的結果決定于過程相關性在時間方向上的變化規律. 沒有考慮間歇過程時段運行的時序性, 因此劃分結果中會出現時間上不連續的具有相似過程相關性的時間片被分在同一個聚類中. 時段劃分結果可讀性有所欠缺, 需要針對劃分結果進行進一步的后續處理. 此外, 該劃分方法根據距離定義衡量過程相關特性的相似度, 聚類的結果受到相似性衡量指標的影響, 而該指標并不能與過程監測的目的直接相關.
                      MPPCA[7475] 一種優化策略, 通過對不同時間點進行不斷嘗試, 分析在該點的劃分所得到的局部模型是否能夠改善原有模型對數據的重構精度, 以此來確定該點的劃分是否合適. 無需過程先驗知識條件, 自動劃分的各個時段時間連續, 解釋性較強. 易陷入局部最優, 導致時段劃分結果不能更好的反映過程特性變化.
                      SSPP[7677] 自動地按照間歇生產過程運行時間順序捕捉潛在過程特性的發展變化, 通過評估時段劃分對監測統計量的影響確定合適的時段劃分點. 無需過程先驗知識條件, 深入考慮了間歇過程潛在特性的時變性和實際過程運行的時序性以及時段劃分結果對于之后監測性能的影響. 對過程時段特性變化的實時捕捉具有一定的時間延遲.
                      下載: 導出CSV

                      表  2  多向分析方法與子時段分析方法對比

                      Table  2  The comparison of multi-way methods and phase partition methods

                      方法 優點 缺點
                      多向分析法 分析方法相對簡單, 直接針對展開的二維數據矩陣進行分析, 可借用傳統的連續過程方法. 針對整個過程只需要建立一個模型. 無法有效分析過程特性時間上的變化規律.
                      子時段分析方法 1)可以更細致地揭示過程運行的潛在特征, 更好地體現過程運行的局部特征, 促進對復雜工業過程的了解;
                      2)在每個子時段可以很容易建立統計分析模型, 結構簡單, 模型實用;
                      3)基于子時段可以很容易建立過程監測模型并實現在線應用而無需預估未知數據;
                      4)可以提高在線故障檢測的精度和靈敏度, 并有利于后續準確的故障隔離和診斷;
                      5)可以深入分析質量指標和每個時段的具體關系, 找出影響質量的關鍵時段和預測變量等關鍵性因素, 有利于產品質量的進一步改進.
                      需要進行時段劃分, 分析過程特性在同一個操作周次內的變化.
                      下載: 導出CSV
                      360彩票
                    • [1] 趙春暉, 陸寧云. 間歇過程統計監測與質量分析. 北京: 科學出版社, 2014.

                      Zhao Chun-Hui, Lu Ning-Yun. Statistical Monitoring and Quality Analysis of Batch Process. Beijing: Science Press, 2014.
                      [2] Nomikos P, MacGregor J F. Monitoring batch processes using multiway principal component analysis. AIChE Journal, 1994, 40(8): 1361?1375 doi: 10.1002/aic.690400809
                      [3] Bhatia T, Biegler L T. Dynamic optimization in the design and scheduling of multiproduct batch plants. Industrial & Engineering Chemistry Research, 1996, 35(7): 2234?2246
                      [4] Méndez C A, Cerdá J, Grossmann I E, Harjunkoski I, Fahl M. State-of-the-art review of optimization methods for short-term scheduling of batch processes. Computers & Chemical Engineering, 2006, 30(6-7): 913?946
                      [5] Lane S, Martin E B, Kooijmans R, Morris A J. Performance monitoring of a multi-product semi-batch process. Journal of Process Control, 2001, 11(1): 1?11 doi: 10.1016/S0959-1524(99)00063-3
                      [6] Edgar T F, Butler S W, Campbell W J, Pfeiffer C, Bode C, Hwang S B, et al. Automatic control in microelectronics manufacturing: Practices, challenges, and possibilities. Automatica, 2000, 36(11): 1567?1603 doi: 10.1016/S0005-1098(00)00084-4
                      [7] 趙春暉, 王福利. 工業過程運行狀態智能監控: 數據驅動方法. 北京: 化學工業出版社, 2019.

                      Zhao Chun-Hui, Wang Fu-Li. Intelligent Monitoring of Industrial Process Operation Status: Data-driven Methods. Beijing: Chemical Industry Press, 2019.
                      [8] Engle R F, Granger C W J. Cointegration and error correction: Representation, estimation and testing. Econometrica, 1987, 55: 251?276 doi: 10.2307/1913236
                      [9] Khediri I B, Limam M, Weihs C. Variable window adaptive Kernel Principal Component Analysis for nonlinear nonstationary process monitoring. Computers & Industrial Engineering, 2011, 61(3): 437?446
                      [10] Liu J L, Chen D S. Nonstationary fault detection and diagnosis for multimode processes. AIChE Journal, 2010, 56(1): 207?219
                      [11] ündey C, Ertun? S, Mistretta T, Looze B. Applied advanced process analytics in biopharmaceutical manufacturing: Challenges and prospects in real-time monitoring and control. Journal of Process Control, 2010, 20(9): 1009?1018 doi: 10.1016/j.jprocont.2010.05.008
                      [12] Qin S J. Process data analytics in the era of big data. AIChE Journal, 2014, 60(9): 3092?3100 doi: 10.1002/aic.14523
                      [13] Chiang L, Lu B, Castillo I. Big data analytics in chemical engineering. Annual Review of Chemical and Biomolecular Engineering, 2017, 8: 63?85 doi: 10.1146/annurev-chembioeng-060816-101555
                      [14] He Q P, Wang J. Statistical process monitoring as a big data analytics tool for smart manufacturing. Journal of Process Control, 2018, 67: 35?43 doi: 10.1016/j.jprocont.2017.06.012
                      [15] 盧靜宜, 曹志興, 高福榮. 批次過程控制—回顧與展望. 自動化學報, 2017, 43(6): 933?943

                      Lu Jing-Yi, Cao Zhi-Xing, Gao Fu-Rong. Batch process control—overview and outlook. Acta Automatica Sinica, 2017, 43(6): 933?943
                      [16] 趙春暉. 多時段間歇過程統計建模、在線監測及質量預報 [博士學位論文], 東北大學, 中國, 2009

                      Zhao Chun-Hui. Statistical Modeling, Online Monitoring and Quality Prediction for Multiphase Batch Processes [Ph.D. dissertation], Northeastern University, China, 2009
                      [17] Shewhart W A. Statistical Method from the Viewpoint of Quality Control. New York, USA: John Dover, 1986.
                      [18] Page E S. Continuous inspection schemes. Biometrika, 1954, 41(1-2): 100?115 doi: 10.1093/biomet/41.1-2.100
                      [19] Page E S. Cumulative sum charts. Technometrics, 1961, 3(1): 1?9 doi: 10.1080/00401706.1961.10489922
                      [20] Roberts S W. Control chart tests based on geometric moving average. Technometrics, 1959, 1(3): 239?250 doi: 10.1080/00401706.1959.10489860
                      [21] Zhao C H, Sun Y X. Multispace total projiection to latent structures and its application to online proceess monitoring, IEEE Transacttions on Control Systeme Technology, 2014, 22(3): 868-883
                      [22] Dunteman G H. Principal Component Analysis. London, UK: SAGE Publication LTD, 1989.
                      [23] Jackson J E. A User′s Guide to Principal Components. New York, USA: Wiley, 1991.
                      [24] 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
                      [25] H?skuldsson A. PLS regression methods. Journal of Chemometrics, 1988, 2(3): 211?228 doi: 10.1002/cem.1180020306
                      [26] 王惠文. 偏最小二乘回歸方法及其應用. 北京: 國防工業出版社, 1999.

                      Wang Hui-Wen. Partial Least-Squares Regression-Method and Applications. Beijing: National Defense Industry Press, 1999.
                      [27] Dayal B S, MacGregor J F. Improved PLS algorithms. Journal of Chemometrics, 1997, 11(1): 73?85 doi: 10.1002/(SICI)1099-128X(199701)11:1<73::AID-CEM435>3.0.CO;2-#
                      [28] Comon P. Independent component analysis, a new concept? Signal Processing, 1994, 36(3): 287?314 doi: 10.1016/0165-1684(94)90029-9
                      [29] Hyv?rinen A, Oja E. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997, 9(7): 1483?1492 doi: 10.1162/neco.1997.9.7.1483
                      [30] Hyv?rinen A, Oja E. Independent component analysis: Algorithms and applications. Neural Networks, 2000, 13(4-5): 411?430 doi: 10.1016/S0893-6080(00)00026-5
                      [31] Kano M, Tanaka S, Hasebe S, Hashimoto I, Ohno H. Monitoring independent components for fault detection. AIChE Journal, 2003, 49(4): 969?976 doi: 10.1002/aic.690490414
                      [32] Smilde A, Bro R, Geladi P. Multi-Way Analysis, Applications in the Chemical Science. New York, USA: Wiley, 2003.
                      [33] Bro R. PARAFAC. Tutorial and applications. Chemometrics and Intelligent Laboratory Systems, 1997, 38(2): 149?171 doi: 10.1016/S0169-7439(97)00032-4
                      [34] Tucker L R. The extension of factor analysis to three-dimensional matrices. Contributions to Mathematical Psychology. New York, USA: Holt, Rinehart and Winston, 1964. 110−162
                      [35] Bro R. Multiway calibration. Multilinear PLS. Journal of Chemometrics, 1996, 10(1): 47?61 doi: 10.1002/(SICI)1099-128X(199601)10:1<47::AID-CEM400>3.0.CO;2-C
                      [36] Sanchez E, Kowalski B R. Tensorial resolution: A direct trilinear decomposition. Journal of Chemometrics, 1990, 4(1): 29?45 doi: 10.1002/cem.1180040105
                      [37] Louwerse D J, Smilde A K. Multivariate statistical process control of batch processes based on three-way models. Chemical Engineering Science, 2000, 55(7): 1225?1235 doi: 10.1016/S0009-2509(99)00408-X
                      [38] Smilde A K. Comments on three-way analyses used for batch process data. Journal of Chemometrics, 2001, 15(1): 19?27 doi: 10.1002/1099-128X(200101)15:1<19::AID-CEM599>3.0.CO;2-F
                      [39] Nomikos P, MacGregor J F. Monitoring batch processes using multiway principal component analysis. AIChE Journal, 1994, 40(8): 1361?1375 doi: 10.1002/aic.690400809
                      [40] Nomikos P, MacGregor J F. Multi-way partial least squares in monitoring batch processes. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 97?108 doi: 10.1016/0169-7439(95)00043-7
                      [41] Nomikos P, MacGregor J F. Multivariate SPC charts for monitoring batch processes. Technometrics, 1995, 37(1): 41?59 doi: 10.1080/00401706.1995.10485888
                      [42] Wold S, Kettaneh N, Fridén H, Holmberg A. Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems, 1998, 44(1-2): 331?340 doi: 10.1016/S0169-7439(98)00162-2
                      [43] Wold S, Geladi P, Esbensen K, ?hman J. Multi-way principal components-and PLS-analysis. Journal of Chemometrics, 1987, 1(1): 41?56 doi: 10.1002/cem.1180010107
                      [44] Wold S, Sj?str?m M. Chemometrics, present and future success. Chemometrics and Intelligent Laboratory Systems, 1998, 44(1-2): 3?14 doi: 10.1016/S0169-7439(98)00075-6
                      [45] Dong D, McAvoy T J. Multi-stage batch process monitoring. In: Proceedings of 1995 American Control Conference (ACC). Seattle, USA: IEEE, 1995. 1857−1861
                      [46] Zheng L L, McAvoy T J, Huang Y B, Chen G. Application of multivariate statistical analysis in batch processes. Industrial & Engineering Chemistry Research, 2001, 40(7): 1641?1649
                      [47] ündey C, Tatara E, ?inar A. Intelligent real-time performance monitoring and quality prediction for batch/fed-batch cultivations. Journal of Biotechnology, 2004, 108(1): 61?77 doi: 10.1016/j.jbiotec.2003.10.004
                      [48] ündey C, ?inar A. Statistical monitoring of multistage, multiphase batch processes. IEEE Control Systems Magazine, 2002, 22(5): 40?52 doi: 10.1109/MCS.2002.1035216
                      [49] ündey C, Ertun? S, ?inar A. Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis. Industrial & Engineering Chemistry Research, 2003, 42(20): 4645?4658
                      [50] Zamprogna E, Barolo M, Seborg D E. Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis. Journal of Process Control, 2005, 15(1): 39?52 doi: 10.1016/j.jprocont.2004.04.006
                      [51] Zamprogna E, Barolo M, Seborg D E. Estimating product composition profiles in batch distillation via partial least squares regression. Control Engineering Practice, 2004, 12(7): 917?929 doi: 10.1016/j.conengprac.2003.11.005
                      [52] Kourti T, Nomikos P, MacGregor J F. Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. Journal of Process Control, 1995, 5(4): 277?284 doi: 10.1016/0959-1524(95)00019-M
                      [53] Martin E B, Morris A J, Papazoglou M C, Kiparissides C. Batch process monitoring for consistent production. Computers & Chemical Engineering, 1996, 20(Suppl 1): S599?S604
                      [54] Martin E B, Morris A J. An overview of multivariate statistical process control in continuous and batch process performance monitoring. Transactions of the Institute of Measurement and Control, 1996, 18(1): 51?60 doi: 10.1177/014233129601800107
                      [55] Lane S, Martin E B, Kooijmans R, Morris A J. Performance monitoring of a multi-product semi-batch process. Journal of Process Control, 2001, 11(1): 1?11 doi: 10.1016/S0959-1524(99)00063-3
                      [56] Meng X, Morris A J, Martin E B. On-line monitoring of batch processes using a PARAFAC representation. Journal of Chemometrics, 2003, 17(1): 65?81 doi: 10.1002/cem.776
                      [57] Lee J M, Yoo C K, Lee I B. Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis. Journal of Biotechnology, 2004, 110(2): 119?136 doi: 10.1016/j.jbiotec.2004.01.016
                      [58] Albazzaz H, Wang X Z. Statistical process control charts for batch operations based on independent component analysis. Industrial & Engineering Chemistry Research, 2004, 43(21): 6731?6741
                      [59] Lee J M, Yoo C K, Lee I B. On-line batch process monitoring using different unfolding method and independent component analysis. Journal of Chemical Engineering of Japan, 2003, 36(11): 1384?1396 doi: 10.1252/jcej.36.1384
                      [60] Albert S, Kinley R D. Multivariate statistical monitoring of batch processes: An industrial case study of fermentation supervision. Trends in Biotechnology, 2001, 19(2): 53?62 doi: 10.1016/S0167-7799(00)01528-6
                      [61] Wong C W L, Escott R, Martin E B, Morris A J. The integration of spectroscopic and process data for enhanced process performance monitoring. The Canadian Journal of Chemical Engineering, 2008, 86(5): 905?923 doi: 10.1002/cjce.20096
                      [62] Lu N Y, Gao F R, Wang F L. A sub-PCA modeling and on-line monitoring strategy for batch processes. AIChE Journal, 2004, 50(1): 255?259 doi: 10.1002/aic.10024
                      [63] Lu N Y, Gao F R. Stage-based process analysis and quality prediction for batch processes. Industrial & Engineering Chemistry Research, 2005, 44(10): 3547?3555
                      [64] Lu N Y, Gao F R. Stage-based online quality control for batch processes. Industrial & Engineering Chemistry Research, 2006, 45(7): 2272?2280
                      [65] Zhao C H, Wang F L, Lu N Y, Jia M X. Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes. Journal of Process Control, 2007, 17(9): 728?741 doi: 10.1016/j.jprocont.2007.02.005
                      [66] Zhao C H, Wang F L, Mao Z H, Lu N Y, Jia M X. Improved knowledge extraction and phase-based quality prediction for batch processes. Industrial & Engineering Chemistry Research, 2008, 47(3): 825?834
                      [67] Zhao C H, Sun Y X. Step-wise sequential phase partition (SSPP) algorithm based statistical modeling and online process monitoring. Chemometrics and Intelligent Laboratory Systems, 2013, 125: 109?120 doi: 10.1016/j.chemolab.2013.03.017
                      [68] Zhao C H. A quality-relevant sequential phase partition approach for regression modeling and quality prediction analysis in manufacturing processes. IEEE Transactions on Automation Science and Engineering, 2014, 11(4): 983?991 doi: 10.1109/TASE.2013.2287347
                      [69] Qin Y, Zhao C H, Gao F R. An Iterative Two-Step Sequential Phase Partition (ITSPP) method for batch process modeling and online monitoring. AIChE Journal, 2016, 62(7): 2358?2373 doi: 10.1002/aic.15205
                      [70] Zhang S M, Zhao C H. Slow-feature-analysis-based batch process monitoring with comprehensive interpretation of operation condition deviation and dynamic anomaly. IEEE Transactions on Industrial Electronics, 2019, 66(5): 3773?3783 doi: 10.1109/TIE.2018.2853603
                      [71] Zhao C H. An iterative within-phase relative analysis algorithm for relative sub-phase modeling and process monitoring. Chemometrics and Intelligent Laboratory Systems, 2014, 134: 67?78 doi: 10.1016/j.chemolab.2014.03.010
                      [72] Kosanovich K A, Piovoso M J, Dahl K S, MacGregor J F, Nomikos P. Multi-way PCA applied to an industrial batch process. In: Proceedings of 1994 American Control Conference (ACC). Baltimore, USA: IEEE, 1994. 1294−1298
                      [73] Kosanovich K A, Dahl K S, Piovoso M J. Improved process understanding using multiway principal component analysis. Industrial & Engineering Chemistry Research, 1996, 35(1): 138?146
                      [74] Lennox B, Hiden H G, Montague G A, Kornfeld G, Goulding P R. Application of multivariate statistical process control to batch operations. Computers & Chemical Engineering, 2000, 24(2-7): 291?296
                      [75] Doan X T, Srinivasan R, Bapat P M, Wangikar P P. Detection of phase shifts in batch fermentation via statistical analysis of the online measurements: A case study with rifamycin B fermentation. Journal of Biotechnology, 2007, 132(2): 156?166 doi: 10.1016/j.jbiotec.2007.06.013
                      [76] Camacho J, Picó J. Online monitoring of batch processes using multi-phase principal component analysis. Journal of Process Control, 2006, 16(10): 1021?1035 doi: 10.1016/j.jprocont.2006.07.005
                      [77] Camacho J, Picó J. Multi-phase principal component analysis for batch processes modelling. Chemometrics and Intelligent Laboratory Systems, 2006, 81(2): 127?136 doi: 10.1016/j.chemolab.2005.11.003
                      [78] Martin E B, Morris A J. Enhanced bio-manufacturing through advanced multivariate statistical technologies. Journal of Biotechnology, 2002, 99(3): 223?235 doi: 10.1016/S0168-1656(02)00212-2
                      [79] Duchesne C, MacGregor J F. Multivariate analysis and optimization of process variable trajectories for batch processes. Chemometrics and Intelligent Laboratory Systems, 2000, 51(1): 125?137 doi: 10.1016/S0169-7439(00)00064-2
                      [80] Rothwell S G, Martin E B, Morris A J. Comparison of methods for dealing with uneven length batches. IFAC Proceedings Volumes, 1998, 31(8): 387?392 doi: 10.1016/S1474-6670(17)40216-3
                      [81] Kassidas A, MacGregor J F, Taylor P A. Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 1998, 44(4): 864?875 doi: 10.1002/aic.690440412
                      [82] Kourti T. Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 2003, 17(1): 93?109 doi: 10.1002/cem.778
                      [83] Yu W K, Zhao C H, Zhang S M. A two-step parallel phase partition algorithm for monitoring multiphase batch processes with limited batches. IFAC-PapersOnLine, 2017, 50(1): 2750?2755 doi: 10.1016/j.ifacol.2017.08.582
                      [84] Itakura F. Minimum prediction residual principle applied to speech recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1975, 23(1): 67?72 doi: 10.1109/TASSP.1975.1162641
                      [85] Sakoe H, Chiba S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1978, 26(1): 43?49 doi: 10.1109/TASSP.1978.1163055
                      [86] Tomasi G, van den Berg F, Andersson C. Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. Journal of Chemometrics, 2004, 18(5): 231?241 doi: 10.1002/cem.859
                      [87] Nielsen N P V, Carstensen J M, Smedsgaard J. Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. Journal of Chromatography A, 1998, 805(1-2): 17?35 doi: 10.1016/S0021-9673(98)00021-1
                      [88] Kourti T, Lee J, MacGregor J F. Experiences with industrial applications of projection methods for multivariate statistical process control. Computers & Chemical Engineering, 1996, 20(1): S745?S750
                      [89] Tates A A, Louwerse D J, Smilde A K, Koot G L M, Berndt H. Monitoring a PVC batch process with multivariate statistical process control charts. Industrial & Engineering Chemistry Research, 1999, 38(12): 4769?4776
                      [90] Neogi D, Schlags C E. Multivariate statistical analysis of an emulsion batch process. Industrial & Engineering Chemistry Research, 1998, 37(10): 3971?3979
                      [91] Kaistha N, Moore C F. Extraction of event times in batch profiles for time synchronization and quality predictions. Industrial & Engineering Chemistry Research, 2001, 40(1): 252?260
                      [92] Lu N, Gao F, Yang Y, Wang F. PCA-based modeling and on-line monitoring strategy for uneven-length batch processes. Industrial & Engineering Chemistry Research, 2004, 43(13): 3343?3352
                      [93] Zhao C H, Mo S Y, Gao F R, Lu N Y, Yao Y. Statistical analysis and online monitoring for handling multiphase batch processes with varying durations. Journal of Process Control, 2011, 21(6): 817?829 doi: 10.1016/j.jprocont.2011.04.005
                      [94] Li W Q, Zhao C H, Gao F R. Sequential time slice alignment based unequal-length phase identification and modeling for fault detection of irregular batches. Industrial & Engineering Chemistry Research, 2015, 54(41): 10020?10030
                      [95] Zhang S M, Zhao C H, Wang S, Wang F L. Pseudo time-slice construction using a variable moving window k nearest neighbor rule for sequential uneven phase division and batch process monitoring. Industrial & Engineering Chemistry Research, 2017, 56(3): 728?740
                      [96] Lu N Y, Yang Y, Wang F L, Gao F R. A stage-based monitoring method for batch processes with limited reference data. IFAC Proceedings Volumes, 2004, 37(9): 787?792 doi: 10.1016/S1474-6670(17)31906-7
                      [97] Zhao C H, Wang F L, Mao Z Z, Lu N Y, Jia M X. Adaptive monitoring based on independent component analysis for multiphase batch processes with limited modeling data. Industrial & Engineering Chemistry Research, 2008, 47(9): 3104?3113
                      [98] Wang Y, Mao Z, Jia M. Feature-points-based Multimodel Single Dynamic Kernel Principle Component Analysis (M-SDKPCA) modeling and online monitoring strategy for uneven-length batch processes. Industrial & Engineering Chemistry Research, 2013, 52(34): 12059?12071
                      [99] Luo L J, Bao S Y, Mao J F, Tang D. Phase partition and phase-based process monitoring methods for multiphase batch processes with uneven durations. Industrial & Engineering Chemistry Research, 2016, 55(7): 2035?2048
                      [100] Tulsyan A, Garvin C, Undey C. Industrial batch process monitoring with limited data. Journal of Process Control, 2019, 77: 114?133 doi: 10.1016/j.jprocont.2019.03.002
                      [101] Zhao C H, Wang F L, Gao F R, Lu N Y, Jia M X. Adaptive monitoring method for batch processes based on phase dissimilarity updating with limited modeling data. Industrial & Engineering Chemistry Research, 2007, 46(14): 4943?4953
                      [102] Kuzborskij I, Orabona F, Caputo B. From N to N+1: Multiclass transfer incremental learning. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE, 2013. 3358?3365
                      [103] Chen C L P, Liu Z L. Broad learning system: An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1): 10?24 doi: 10.1109/TNNLS.2017.2716952
                      [104] Yu W K, Zhao C H. Broad convolutional neural network based industrial process fault diagnosis with incremental learning capability. IEEE Transactions on Industrial Electronics, 2020, 67(6): 5081?5091 doi: 10.1109/TIE.2019.2931255
                      [105] Zhao C H, Wang W, Qin Y, Gao F R. Comprehensive subspace decomposition with analysis of between-mode relative changes for multimode process monitoring. Industrial & Engineering Chemistry Research, 2015, 54(12): 3154?3166
                      [106] Zhao C H. Concurrent phase partition and between-mode statistical analysis for multimode and multiphase batch process monitoring. AIChE Journal, 2014, 60(2): 559?573 doi: 10.1002/aic.14282
                      [107] Zhang S M, Zhao C H, Gao F R. Two-directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase batch process monitoring with uneven lengths. Chemical Engineering Science, 2018, 178: 104?117 doi: 10.1016/j.ces.2017.12.025
                      [108] Zhao C H. Phase analysis and statistical modeling with limited batches for multimode and multiphase process monitoring. Journal of Process Control, 2014, 24(6): 856?870 doi: 10.1016/j.jprocont.2014.04.001
                      [109] Kramer M A. Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 1991, 37(2): 233?243 doi: 10.1002/aic.690370209
                      [110] Dong D, McAvoy T J. Batch tracking via nonlinear principal component analysis. AIChE Journal, 1996, 42(8): 2199?2208 doi: 10.1002/aic.690420810
                      [111] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504?507 doi: 10.1126/science.1127647
                      [112] 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
                      [113] Lee J M, Yoo C K, Choi S W, Vanrolleghem P A, Lee I B. Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 2004, 59(1): 223?234 doi: 10.1016/j.ces.2003.09.012
                      [114] Kruger U, Antory D, Hahn J, Irwin G W, McCullough G. Introduction of a nonlinearity measure for principal component models. Computers & Chemical Engineering, 2005, 29(11-12): 2355?2362
                      [115] Zhang S M, Wang F L, Zhao L P, Wang S, Chang Y Q. A novel strategy of the data characteristics test for selecting a process monitoring method automatically. Industrial & Engineering Chemistry Research, 2016, 55(6): 1642?1654
                      [116] Li W Q, Zhao C H, Gao F R. Linearity evaluation and variable subset partition based hierarchical process modeling and monitoring. IEEE Transactions on Industrial Electronics, 2018, 65(3): 2683?2692 doi: 10.1109/TIE.2017.2745452
                      [117] Yan W W, Guo P J, Gong L, Li Z K. Nonlinear and robust statistical process monitoring based on variant autoencoders. Chemometrics and Intelligent Laboratory Systems, 2016, 158: 31?40 doi: 10.1016/j.chemolab.2016.08.007
                      [118] Zhang X, Yan W W, Zhao X, Shao H H. Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis. Process Biochemistry, 2007, 42(8): 1200?1210 doi: 10.1016/j.procbio.2007.05.016
                      [119] Zhao C H, Gao F R, Wang F L. Nonlinear batch process monitoring using phase-based kernel-independent component analysis-Principal Component Analysis (KICA-PCA). Industrial & Engineering Chemistry Research, 2009, 48(20): 9163?9174
                      [120] Rashid M M, Yu J. Nonlinear and non-Gaussian dynamic batch process monitoring using a new multiway kernel independent component analysis and multidimensional mutual information based dissimilarity approach. Industrial & Engineering Chemistry Research, 2012, 51(33): 10910?10920
                      [121] Onel M, Kieslich C A, Guzman Y A, Floudas C A, Pistikopoulos E N. Big data approach to batch process monitoring: Simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection. Computers & Chemical Engineering, 2018, 115: 46?63
                      [122] 劉育明. 動態過程數據的多變量統計監控方法研究 [博士學位論文], 浙江大學, 中國, 2006

                      Liu Yu-Ming. Multivariate Statistical Monitoring Methods for Dynamic Process Data [Ph.D. dissertation], Zhejiang University, China, 2006
                      [123] Wang Y J, Sun F M, Li B. Multiscale neighborhood normalization-based multiple dynamic PCA monitoring method for batch processes with frequent operations. IEEE Transactions on Automation Science and Engineering, 2018, 15(3): 1053?1064 doi: 10.1109/TASE.2017.2713800
                      [124] Dong Y N, Qin S J. A novel dynamic PCA algorithm for dynamic data modeling and process monitoring. Journal of Process Control, 2018, 67: 1?11 doi: 10.1016/j.jprocont.2017.05.002
                      [125] Dong Y N, Qin S J. Regression on dynamic PLS structures for supervised learning of dynamic data. Journal of Process Control, 2018, 68: 64?72 doi: 10.1016/j.jprocont.2018.04.006
                      [126] Chen J H, Liu K C. On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chemical Engineering Science, 2002, 57(1): 63?75 doi: 10.1016/S0009-2509(01)00366-9
                      [127] Hu K L, Yuan J Q. Statistical monitoring of fed-batch process using dynamic multiway neighborhood preserving embedding. Chemometrics and Intelligent Laboratory Systems, 2008, 90(2): 195?203 doi: 10.1016/j.chemolab.2007.10.002
                      [128] Stubbs S, Zhang J, Morris J. Fault detection in dynamic processes using a simplified monitoring-specific CVA state space modelling approach. Computers & Chemical Engineering, 2012, 41: 77?87
                      [129] Lu Q G, Jiang B B, Gopaluni R B, Loewen P D, Braatz R D. Sparse canonical variate analysis approach for process monitoring. Journal of Process Control, 2018, 71: 90?102 doi: 10.1016/j.jprocont.2018.09.009
                      [130] 鄒筱瑜, 王福利, 常玉清, 鄭偉. 基于兩層分塊GMM-PRS的流程工業過程運行狀態評價. 自動化學報, 2019, 45(11): 2071?2081

                      Zou Xiao-Yu, Wang Fu-Li, Chang Yu-Qing, Zheng Wei. Plant-wide process operating performance assessment based on two-level multi-block GMM-PRS. Acta Automatica Sinica, 2019, 45(11): 2071?2081
                      [131] Fan L, Kodamana H, Huang B. Semi-supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach. AIChE Journal, 2019, 65(3): 964?979 doi: 10.1002/aic.16481
                      [132] Shang C, Huang B, Yang F, Huang D X. Slow feature analysis for monitoring and diagnosis of control performance. Journal of Process Control, 2016, 39: 21?34 doi: 10.1016/j.jprocont.2015.12.004
                      [133] Zhang S M, Zhao C H, Huang B. Simultaneous static and dynamic analysis for fine-scale identification of process operation statuses. IEEE Transactions on Industrial Informatics, 2019, 15(9): 5320?5329 doi: 10.1109/TII.2019.2896987
                      [134] Zou X Y, Zhao C H. Concurrent assessment of process operating performance with joint static and dynamic analysis. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2776?2786 doi: 10.1109/TII.2019.2934757
                      [135] Li W Q, Zhao C H, Huang B. Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control. Industrial & Engineering Chemistry Research, 2018, 57(46): 15759?15772
                      [136] Zhao C H, Huang B. A Full-condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis. AIChE Journal, 2018, 64(5): 1662?1681 doi: 10.1002/aic.16048
                      [137] Choi S W, Morris J, Lee I B. Dynamic model-based batch process monitoring. Chemical Engineering Science, 2008, 63(3): 622?636 doi: 10.1016/j.ces.2007.09.046
                      [138] Zheng J L, Zhao C H. Online monitoring of performance variations and process dynamic anomalies with performance-relevant full decomposition of slow feature analysis. Journal of Process Control, 2019, 80: 89?102 doi: 10.1016/j.jprocont.2019.05.004
                      [139] Yu W K, Zhao C H. Recursive exponential slow feature analysis for fine-scale adaptive processes monitoring with comprehensive operation status identification. IEEE Transactions on Industrial Informatics, 2019, 15(6): 3311?3323 doi: 10.1109/TII.2018.2878405
                      [140] 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
                      [141] Duda R O, Hart P E. Pattern Classification and Scene Analysis. New York: USA: Wiley, 1973
                      [142] Yu W K, Zhao C H. Sparse exponential discriminant analysis and its application to fault diagnosis. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5931?5940 doi: 10.1109/TIE.2017.2782232
                      [143] Yu W K, Zhao C H. Online fault diagnosis in industrial processes using multimodel exponential discriminant analysis algorithm. IEEE Transactions on Control Systems Technology, 2019, 27(3): 1317?1325 doi: 10.1109/TCST.2017.2789188
                      [144] Chai Z, Zhao C H. A fine-grained adversarial network method for cross-domain industrial fault diagnosis. IEEE Transactions on Automation Science and Engineering, 2020, 17(3): 1432?1442 doi: 10.1109/TASE.2019.2957232
                      [145] Chiang L H, Kotanchek M E, Kordon A K. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers & Chemical Engineering, 2004, 28(8): 1389?1401
                      [146] Chai Z, Zhao C H. Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification. IEEE Transactions on Industrial Informatics, 2020, 16(1): 54?66 doi: 10.1109/TII.2019.2915559
                      [147] Yu W K, Zhao C H. Online fault diagnosis for industrial processes with Bayesian network-based probabilistic ensemble learning strategy. IEEE Transactions on Automation Science and Engineering, 2019, 16(4): 1922?1932 doi: 10.1109/TASE.2019.2915286
                      [148] Westerhuis J A, Gurden S P, Smilde A K. Generalized contribution plots in multivariate statistical process monitoring. Chemometrics and Intelligent Laboratory Systems, 2000, 51(1): 95?114 doi: 10.1016/S0169-7439(00)00062-9
                      [149] Alcala C F, Qin S J. Reconstruction-based contribution for process monitoring with kernel principal component analysis. Industrial & Engineering Chemistry Research, 2010, 49(71): 7849?7857
                      [150] Dunia R, Qin S J. Subspace approach to multidimensional fault identification and reconstruction. AIChE Journal, 1998, 44(8): 1813?1831 doi: 10.1002/aic.690440812
                      [151] Zhao C H, Sun Y X. Subspace decomposition approach of fault deviations and its application to fault reconstruction. Control Engineering Practice, 2013, 21(10): 1396?1409 doi: 10.1016/j.conengprac.2013.06.008
                      [152] Zhao C H, Gao F R. Online fault prognosis with relative deviation analysis and vector autoregressive modeling. Chemical Engineering Science, 2015, 138: 531?543 doi: 10.1016/j.ces.2015.08.037
                      [153] Zhao C H, Gao F R. Subspace decomposition-based reconstruction modeling for fault diagnosis in multiphase batch processes. Industrial & Engineering Chemistry Research, 2013, 52(41): 14613?14626
                      [154] Zhao C H, Zhang W D. Reconstruction based fault diagnosis using concurrent phase partition and analysis of relative changes for multiphase batch processes with limited fault batches. Chemometrics and Intelligent Laboratory Systems, 2014, 130: 135?150 doi: 10.1016/j.chemolab.2013.10.014
                      [155] Sun H, Zhang S M, Zhao C H, Gao F R. A sparse reconstruction strategy for online fault diagnosis in nonstationary processes with no a priori fault information. Industrial & Engineering Chemistry Research, 2017, 56(24): 6993?7008
                      [156] Wu J, Zhao J S. Deep convolutional neural network model based chemical process fault diagnosis. Computers & Chemical Engineering, 2018, 115: 185?197
                      [157] Peng K X, Zhang K, You B, Dong J, Wang Z D. A quality-based nonlinear fault diagnosis framework focusing on industrial multimode batch processes. IEEE Transactions on Industrial Electronics, 2016, 63(4): 2615?2624
                      [158] Zhao X Q, Wang T. Tensor dynamic neighborhood preserving embedding algorithm for fault diagnosis of batch process. Chemometrics and Intelligent Laboratory Systems, 2017, 162: 94?103 doi: 10.1016/j.chemolab.2017.01.007
                      [159] Yang C M, Hou J. Fed-batch fermentation penicillin process fault diagnosis and detection based on support vector machine. Neurocomputing, 2016, 190: 117?123 doi: 10.1016/j.neucom.2016.01.027
                      [160] Cerrada M, Zurita G, Cabrera D, Sánchez R V, Artés M, Li C. Fault diagnosis in spur gears based on genetic algorithm and random forest. Mechanical Systems and Signal Processing, 2016, 70-71: 87?103 doi: 10.1016/j.ymssp.2015.08.030
                      [161] Zou X Y, Wang F L, Chang Y Q. Assessment of operating performance using cross-domain feature transfer learning. Control Engineering Practice, 2019, 89: 143?153 doi: 10.1016/j.conengprac.2019.05.007
                      [162] 周東華, 劉洋, 何瀟. 閉環系統故障診斷技術綜述. 自動化學報, 2013, 39(11): 1933?1943 doi: 10.3724/SP.J.1004.2013.01933

                      Zhou Dong-Hua, Liu Yang, He Xiao. Review on fault diagnosis techniques for closed-loop systems. Acta Automatica Sinica, 2013, 39(11): 1933?1943 doi: 10.3724/SP.J.1004.2013.01933
                      [163] Zou X Y, Zhao C H. Meticulous assessment of operating performance for processes with a hybrid of stationary and nonstationary variables. Industrial & Engineering Chemistry Research, 2019, 58(3): 1341?1351
                      [164] Zhao C H, Gao F R. Fault subspace selection approach combined with analysis of relative changes for reconstruction modeling and multifault diagnosis. IEEE Transactions on Control Systems Technology, 2016, 24(3): 928?939 doi: 10.1109/TCST.2015.2464331
                      [165] Qin Y, Zhao C H, Gao F R. An intelligent non-optimality self-recovery method based on reinforcement learning with small data in big data era. Chemometrics and Intelligent Laboratory Systems, 2018, 176: 89?100 doi: 10.1016/j.chemolab.2018.03.010
                      [166] Sutton R S, Barto A G. Reinforcement Learning: An Introduction (Second edition). Cambridge: MIT Press, 2018.
                      [167] Lewis F L, Vrabie D. Reinforcement learning and adaptive dynamic programming for feedback control. IEEE Circuits and Systems Magazine, 2009, 9(3): 32?50 doi: 10.1109/MCAS.2009.933854
                      [168] Wu H, Zhao J S. Deep convolutional neural network model based chemical process fault diagnosis. Computers & Chemical Engineering, 2018, 115: 185?197
                      [169] Wang H, Yuan Z L, Chen Y B, Shen B Y, Wu A X. An industrial missing values processing method based on generating model. Computer Networks, 2019, 158: 61?68 doi: 10.1016/j.comnet.2019.02.007
                      [170] Yuan X F, Li L, Wang Y L. Nonlinear dynamic soft sensor modeling with supervised long short-term memory network. IEEE Transactions on Industrial Informatics, 2020, 16(5): 3168?3176 doi: 10.1109/TII.2019.2902129
                    • 加載中
                    圖(6) / 表(2)
                    計量
                    • 文章訪問數:  4677
                    • HTML全文瀏覽量:  7924
                    • PDF下載量:  288
                    • 被引次數: 0
                    出版歷程
                    • 收稿日期:  2019-08-25
                    • 錄用日期:  2019-12-02
                    • 網絡出版日期:  2019-12-31
                    • 刊出日期:  2020-10-29

                    目錄

                      /

                      返回文章
                      返回