A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

Safe and stable operation plays an important role in the chemical industry. Fault detection and diagnosis (FDD) make it possible to identify abnormal process deviations early and assist operators in taking proper action against fault propagation. After decades of development, data-driven process mon...

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Published inProcesses Vol. 10; no. 2; p. 335
Main Authors Ji, Cheng, Sun, Wei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2022
Subjects
Artificial intelligence
Carbon
Chemical industry
Data analysis
Data processing
Datasets
Distributed control systems
False alarms
Fault detection
Fault diagnosis
Industrial applications
Monitoring
Reviews
Simulation
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Abstract Safe and stable operation plays an important role in the chemical industry. Fault detection and diagnosis (FDD) make it possible to identify abnormal process deviations early and assist operators in taking proper action against fault propagation. After decades of development, data-driven process monitoring technologies have gradually attracted attention from process industries. Although many promising FDD methods have been proposed from both academia and industry, challenges remain due to the complex characteristics of industrial data. In this work, classical and recent research on data-driven process monitoring methods is reviewed from the perspective of characterizing and mining industrial data. The implementation framework of data-driven process monitoring methods is first introduced. State of art of process monitoring methods corresponding to common industrial data characteristics are then reviewed. Finally, the challenges and possible solutions for actual industrial applications are discussed.
AbstractList Safe and stable operation plays an important role in the chemical industry. Fault detection and diagnosis (FDD) make it possible to identify abnormal process deviations early and assist operators in taking proper action against fault propagation. After decades of development, data-driven process monitoring technologies have gradually attracted attention from process industries. Although many promising FDD methods have been proposed from both academia and industry, challenges remain due to the complex characteristics of industrial data. In this work, classical and recent research on data-driven process monitoring methods is reviewed from the perspective of characterizing and mining industrial data. The implementation framework of data-driven process monitoring methods is first introduced. State of art of process monitoring methods corresponding to common industrial data characteristics are then reviewed. Finally, the challenges and possible solutions for actual industrial applications are discussed.
Author Sun, Wei
Ji, Cheng
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Cites_doi 10.1023/A:1010933404324
10.1016/j.ces.2007.09.046
10.1016/j.jprocont.2017.05.002
10.1016/j.isatra.2020.07.037
10.1016/j.ces.2018.01.036
10.1021/acs.iecr.5b02266
10.1016/j.compchemeng.2009.08.007
10.1016/j.chemolab.2009.01.001
10.1016/S0098-1354(02)00127-8
10.1021/ie901911p
10.1109/TCST.2006.883234
10.3390/pr10010169
10.1109/DDCLS49620.2020.9275054
10.1016/j.jlp.2016.08.020
10.1016/S0169-7439(00)00058-7
10.1016/j.ces.2011.10.011
10.1016/S0098-1354(00)00509-3
10.1021/acs.iecr.6b01916
10.1016/j.chemolab.2015.04.016
10.1016/j.automatica.2009.02.027
10.1016/0169-7439(95)00076-3
10.1016/j.isatra.2013.11.007
10.1162/089976698300017467
10.1016/j.psep.2008.06.004
10.1016/j.jprocont.2003.09.004
10.1021/ie4039345
10.3390/pr5030035
10.1016/j.chemolab.2012.05.010
10.1016/j.chemolab.2013.10.014
10.1016/S0098-1354(02)00162-X
10.1016/j.chemolab.2021.104371
10.1016/j.jprocont.2019.01.005
10.1021/ie900479g
10.1016/j.isatra.2019.05.013
10.1016/j.jprocont.2014.12.001
10.1021/ie00103a031
10.7551/mitpress/1120.003.0080
10.1021/acs.iecr.7b03600
10.1080/00224065.1996.11979699
10.3182/20120710-4-SG-2026.00172
10.1016/j.jprocont.2017.09.003
10.1016/j.automatica.2014.09.005
10.1016/j.arcontrol.2020.09.004
10.1109/ICDM.2008.17
10.1162/089976602317318938
10.1016/j.cie.2020.106376
10.1016/j.ssci.2019.104580
10.1002/cjce.5450690105
10.1002/bit.21220
10.1021/ie300679e
10.3390/pr10010122
10.1016/j.chemolab.2019.03.012
10.1016/j.arcontrol.2009.08.001
10.1016/j.ins.2013.06.021
10.1016/j.jprocont.2010.12.003
10.1016/B978-0-12-823377-1.50195-6
10.1016/j.ifacol.2015.09.595
10.1016/j.conengprac.2013.06.017
10.1016/j.jprocont.2018.02.005
10.1109/SYSTOL.2010.5676081
10.1002/aic.16489
10.1021/ie9018947
10.1016/j.jprocont.2011.02.004
10.1016/j.compchemeng.2019.04.003
10.3182/20090630-4-ES-2003.00184
10.1016/j.ces.2004.08.007
10.1016/j.arcontrol.2016.09.001
10.1016/j.compchemeng.2019.106515
10.1016/S1004-9541(06)60103-1
10.1002/cjce.23738
10.1016/j.jprocont.2020.09.005
10.1016/j.chemolab.2005.11.003
10.1016/j.ifacol.2018.09.378
10.1021/ie070741+
10.1016/j.chemolab.2015.05.019
10.3390/su10082935
10.1016/j.engappai.2019.04.013
10.1111/j.1467-9868.2009.00723.x
10.1021/ie102564d
10.1016/j.jprocont.2016.09.007
10.1021/acs.iecr.7b01642
10.1016/j.jlp.2012.03.001
10.1016/j.dib.2020.105779
10.1126/science.290.5500.2323
10.1016/j.jprocont.2015.05.004
10.1016/j.compchemeng.2021.107587
10.1016/j.compchemeng.2018.04.009
10.1021/ie102048f
10.1016/j.measurement.2021.110064
10.1016/j.compchemeng.2010.05.004
10.1021/ie000141+
10.1016/j.microrel.2016.07.151
10.1016/j.compchemeng.2017.03.026
10.1021/ie070381q
10.1016/S0967-0661(99)00040-4
10.3182/20110828-6-IT-1002.00934
10.1016/j.jprocont.2006.07.005
10.1016/S0169-7439(98)00162-2
10.1016/S0098-1354(02)00161-8
10.1109/TIE.2018.2811358
10.1016/j.ces.2010.08.024
10.1016/S0098-1354(01)00683-4
10.1016/j.ifacol.2017.08.2208
10.1016/j.jprocont.2018.09.009
10.1109/ACCESS.2019.2956494
10.1016/j.chemolab.2015.08.025
10.1016/j.chemolab.2014.04.001
10.3182/20140824-6-ZA-1003.00754
10.1177/0020294020911390
10.1016/j.ces.2004.07.019
10.1016/j.chemolab.2017.09.021
10.1016/j.conengprac.2020.104692
10.1002/aic.16048
10.1016/S0967-0661(99)00038-6
10.1145/2689746.2689747
10.1016/j.jprocont.2016.01.001
10.1002/cjce.23740
10.1016/j.chemolab.2011.10.013
10.1016/j.compchemeng.2020.106762
10.1021/ie8012874
10.1016/j.compchemeng.2020.106978
10.1016/j.jprocont.2018.02.004
10.1080/07408170903019150
10.1016/j.jprocont.2019.09.004
10.1016/j.ifacol.2016.07.259
10.1021/ie801611s
10.1016/j.arcontrol.2012.09.004
10.1023/B:MACH.0000008084.60811.49
10.1016/S0169-7439(00)00062-9
10.1016/j.ces.2003.09.012
10.1016/j.jprocont.2007.11.007
10.1016/j.jprocont.2021.07.007
10.1021/ie202720y
10.1016/j.jprocont.2017.03.005
10.1016/S0967-0661(02)00096-5
10.1016/0098-1354(93)80018-I
10.1016/j.ifacol.2015.09.589
10.1016/j.ces.2018.05.045
10.1002/aic.690480610
10.1002/aic.690490414
10.1002/cjce.5450850414
10.1016/j.jprocont.2005.12.002
10.1016/j.conengprac.2018.11.020
10.1021/acs.iecr.5b04777
10.1007/BF00994018
10.1021/acs.iecr.7b00011
10.1016/j.compchemeng.2017.05.029
10.1016/S0098-1354(97)00262-7
10.1016/j.chemolab.2020.104230
10.1016/j.measurement.2020.108782
10.1021/ie048873f
10.1080/00401706.1995.10485888
10.1002/aic.690420412
10.1016/S1474-6670(17)57142-6
10.1109/INDIN.2017.8104910
10.1016/j.jprocont.2015.11.004
10.1016/j.jprocont.2008.11.001
10.1016/j.psep.2021.10.036
10.1002/aic.10024
10.1103/PhysRevE.97.052216
10.1016/j.chemolab.2017.07.013
10.1016/j.ces.2018.10.024
10.1002/aic.690400809
10.1002/aic.690430810
10.1016/j.compchemeng.2004.02.036
10.1145/1390156.1390294
10.3390/pr9061027
10.1080/00224065.1992.12015232
10.1016/S0009-2509(01)00366-9
10.1016/j.jprocont.2019.01.008
10.1016/0169-7439(95)00043-7
10.1016/j.compchemeng.2020.107064
10.1016/j.compchemeng.2003.09.031
10.1021/ie302069q
10.1002/aic.690490113
10.1109/TII.2017.2695583
10.1016/j.chemolab.2016.09.006
10.1002/aic.690440712
10.1016/j.isatra.2018.10.016
10.1016/j.conengprac.2016.09.014
10.1016/j.compchemeng.2020.107024
10.1016/j.psep.2020.10.024
10.1021/acs.iecr.5b00373
10.1016/j.jprocont.2016.01.011
10.1021/acs.iecr.9b02391
10.1016/j.ssci.2020.104741
10.1002/cem.2686
10.1016/j.jprocont.2020.06.013
10.1080/00401706.1979.10489779
10.1198/106186006X113430
10.1126/science.1127647
10.1016/j.chemolab.2014.08.007
10.1016/j.jprocont.2012.06.008
10.1016/S0009-2509(02)00338-X
10.1021/ie0497893
10.1016/S0098-1354(02)00160-6
10.1002/aic.11515
10.1109/MCS.2002.1035216
10.1016/j.conengprac.2012.11.013
10.1002/aic.14888
10.3182/20120710-4-SG-2026.00033
10.1007/978-1-4471-5185-2
10.1016/S0098-1354(01)00738-4
10.1109/TCST.2019.2897946
10.1016/j.jprocont.2013.09.017
10.1109/ACCESS.2017.2672780
10.1016/j.compchemeng.2021.107252
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References ref_137
Duan (ref_126) 2020; 31
Verde (ref_18) 2019; 189
Qin (ref_35) 2009; 42
Shao (ref_113) 2009; 96
Li (ref_211) 2016; 159
Kano (ref_23) 2002; 26
Chen (ref_98) 2021; 107
Wang (ref_129) 2005; 44
Pistikopoulos (ref_7) 2021; 147
Yao (ref_172) 2009; 33
An (ref_106) 2015; 2
Nomikos (ref_161) 1995; 30
Lee (ref_43) 2004; 14
Sukchotrat (ref_62) 2009; 42
ref_127
Han (ref_151) 2018; 51
Lee (ref_212) 2020; 142
Galicia (ref_48) 2012; 45
ref_120
Dong (ref_80) 2018; 67
ref_124
Cao (ref_150) 2021; 210
Tax (ref_61) 2004; 54
Zhang (ref_93) 2008; 47
Lin (ref_140) 2019; 84
Shang (ref_133) 2018; 65
Nomikos (ref_159) 1994; 40
Liu (ref_184) 2018; 181
Qin (ref_12) 2012; 36
Zhang (ref_148) 2015; 54
Engle (ref_134) 1987; 55
Ji (ref_188) 2020; 81
Yu (ref_45) 2008; 54
Gao (ref_57) 2020; 98
Li (ref_79) 2011; 44
He (ref_112) 2004; 16
Wold (ref_162) 1998; 44
Camacho (ref_177) 2006; 81
Xu (ref_199) 2013; 52
Jung (ref_1) 2020; 124
Ning (ref_197) 2015; 48
Shang (ref_83) 2015; 61
Choi (ref_44) 2004; 28
Zheng (ref_81) 2022; 157
Tong (ref_216) 2019; 75
Zhang (ref_108) 2019; 75
Zhao (ref_200) 2014; 130
Zeng (ref_26) 2014; 50
Cheng (ref_27) 2019; 129
Gharahbagheri (ref_203) 2017; 56
Kano (ref_24) 2002; 48
Ge (ref_15) 2017; 171
Zhao (ref_179) 2009; 48
Venkatasubramanian (ref_9) 2003; 27
Undey (ref_171) 2002; 22
Yao (ref_182) 2009; 19
ref_214
ref_213
Gajjar (ref_50) 2016; 49
Alcala (ref_92) 2010; 49
ref_217
Zou (ref_51) 2006; 15
Luo (ref_71) 2014; 53
Wang (ref_187) 2009; 87
Yue (ref_30) 2001; 40
Yu (ref_116) 2012; 22
Zhang (ref_105) 2018; 64
Wang (ref_47) 2010; 49
Wang (ref_130) 2020; 107
Bi (ref_110) 2021; 156
Gao (ref_86) 2021; 105
Ramaker (ref_170) 2002; 57
Bao (ref_118) 2016; 47
He (ref_114) 2016; 37
Yan (ref_189) 2015; 146
Yu (ref_181) 2009; 48
Yu (ref_180) 2012; 68
Sun (ref_38) 2003; 49
Modak (ref_6) 2020; 141
Jiang (ref_87) 2019; 58
Venkatasubramanian (ref_10) 2003; 27
Hiranmayee (ref_186) 1999; 7
Wiskott (ref_82) 2002; 14
Qin (ref_16) 2019; 126
Zhu (ref_104) 2021; 171
Wan (ref_101) 2019; 7
Wang (ref_155) 2012; 110
Ghosh (ref_28) 2011; 35
Hwang (ref_17) 1999; 7
Cho (ref_91) 2005; 60
Lu (ref_53) 2018; 71
Chen (ref_163) 2002; 57
Zhao (ref_142) 2004; 43
Jackson (ref_21) 1979; 21
Zheng (ref_85) 2020; 95
Venkatasubramanian (ref_11) 2003; 27
ref_111
Choi (ref_164) 2008; 63
Zhang (ref_166) 2007; 46
Zhang (ref_4) 2012; 25
Tong (ref_32) 2017; 58
Apsemidis (ref_88) 2020; 142
Zhou (ref_125) 2016; 65
Wang (ref_156) 2016; 55
Bauer (ref_209) 2007; 15
Chen (ref_46) 2010; 34
Harrou (ref_25) 2016; 44
Song (ref_63) 2015; 27
Wang (ref_132) 2003; 11
Lee (ref_109) 2019; 83
Birol (ref_160) 2002; 26
Zhang (ref_115) 2011; 50
Conlin (ref_193) 2000; 14
Lin (ref_139) 2017; 56
ref_107
Kano (ref_42) 2003; 49
ref_102
Jiang (ref_40) 2015; 32
Nomikos (ref_173) 1995; 37
Zhao (ref_138) 2017; 64
Godoy (ref_94) 2014; 135
Yuan (ref_206) 2012; 45
Wang (ref_158) 2017; 57
Qin (ref_128) 1999; 32
ref_13
Choi (ref_31) 2004; 59
Severson (ref_14) 2016; 42
Westerhuis (ref_194) 2000; 51
Li (ref_73) 2006; 14
Ge (ref_5) 2013; 52
Bakshi (ref_37) 1998; 44
Alcala (ref_195) 2009; 45
Huang (ref_84) 2017; 169
Chen (ref_208) 2017; 50
Roweis (ref_70) 2000; 290
Luo (ref_215) 2017; 106
Dorgo (ref_64) 2021; 149
Ge (ref_176) 2011; 21
Wu (ref_141) 2020; 141
Wang (ref_157) 2015; 148
Li (ref_76) 2019; 95
Peng (ref_196) 2013; 21
Raich (ref_29) 1996; 42
Qin (ref_78) 2020; 50
Yu (ref_103) 2020; 28
Liu (ref_66) 2018; 64
Li (ref_75) 2021; 216
Zhao (ref_143) 2006; 16
Zhao (ref_174) 2014; 138
Huang (ref_77) 2019; 85
He (ref_123) 2015; 145
Kano (ref_22) 2001; 25
Ha (ref_146) 2017; 106
Yu (ref_136) 2020; 92
Lee (ref_90) 2004; 59
Ku (ref_72) 1995; 30
Tan (ref_145) 2011; 51
Ma (ref_152) 2012; 118
Yoo (ref_144) 2007; 96
Smola (ref_89) 1998; 10
Amin (ref_204) 2019; 195
Venkatasubramanian (ref_55) 2019; 65
Luo (ref_117) 2015; 54
ref_58
Chang (ref_185) 1990; 29
Kano (ref_19) 2000; 24
Kresta (ref_36) 1991; 69
ref_54
Fan (ref_96) 2014; 259
Huang (ref_99) 2016; 39
Tong (ref_147) 2013; 23
Lee (ref_165) 2004; 28
Zhao (ref_154) 2010; 65
Gao (ref_167) 2020; 98
Zhan (ref_119) 2017; 56
ref_59
Russell (ref_74) 2000; 51
Hinton (ref_100) 2006; 313
Fan (ref_97) 2014; 22
Chun (ref_52) 2010; 72
Jiang (ref_192) 2015; 26
Tracy (ref_20) 2018; 24
ref_68
Camacho (ref_178) 2006; 16
Kourti (ref_191) 2018; 28
Hui (ref_169) 2020; 53
Lee (ref_95) 2007; 85
Wang (ref_153) 2015; 29
Chen (ref_135) 2009; 48
Li (ref_207) 2018; 97
Wang (ref_69) 2021; 185
Bauer (ref_205) 2008; 18
Hajihosseini (ref_210) 2014; 53
Li (ref_122) 2014; 47
Puggini (ref_67) 2015; 48
Cortes (ref_60) 1995; 20
Zeng (ref_33) 2019; 83
Zhang (ref_168) 2017; 5
Gajjar (ref_49) 2018; 67
Breiman (ref_65) 2001; 45
Lu (ref_175) 2004; 50
Chen (ref_3) 2020; 128
Amin (ref_202) 2018; 189
Downs (ref_34) 1993; 17
Wu (ref_56) 2018; 115
Kwak (ref_121) 2020; 135
Cai (ref_201) 2017; 13
Miller (ref_190) 1998; 8
Negiz (ref_39) 1997; 43
Qin (ref_131) 1998; 22
Xie (ref_149) 2012; 51
ref_2
Ji (ref_198) 2016; 40
Jiang (ref_41) 2017; 58
Sun (ref_183) 2011; 21
ref_8
References_xml – volume: 45
  start-page: 5
  year: 2001
  ident: ref_65
  article-title: Random forests
  publication-title: Mach. Learn.
  doi: 10.1023/A:1010933404324
– volume: 63
  start-page: 622
  year: 2008
  ident: ref_164
  article-title: Dynamic model-based batch process monitoring
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/j.ces.2007.09.046
– volume: 67
  start-page: 1
  year: 2018
  ident: ref_80
  article-title: A novel dynamic PCA algorithm for dynamic data modeling and process monitoring
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2017.05.002
– volume: 107
  start-page: 360
  year: 2020
  ident: ref_130
  article-title: Recursive correlated representation learning for adaptive monitoring of slowly varying processes
  publication-title: ISA Trans.
  doi: 10.1016/j.isatra.2020.07.037
– volume: 181
  start-page: 101
  year: 2018
  ident: ref_184
  article-title: Sequential local-based Gaussian mixture model for monitoring multiphase batch processes
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/j.ces.2018.01.036
– volume: 54
  start-page: 11126
  year: 2015
  ident: ref_117
  article-title: Nonlinear Process Monitoring Using Data-Dependent Kernel Global–Local Preserving Projections
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.5b02266
– volume: 34
  start-page: 500
  year: 2010
  ident: ref_46
  article-title: On-line multivariate statistical monitoring of batch processes using Gaussian mixture model
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2009.08.007
– volume: 96
  start-page: 75
  year: 2009
  ident: ref_113
  article-title: Generalized orthogonal locality preserving projections for nonlinear fault detection and diagnosis
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2009.01.001
– volume: 26
  start-page: 1553
  year: 2002
  ident: ref_160
  article-title: A modular simulation package for fed-batch fermentation: Penicillin production
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/S0098-1354(02)00127-8
– volume: 49
  start-page: 7858
  year: 2010
  ident: ref_47
  article-title: Multivariate statistical process monitoring based on statistics pattern analysis
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie901911p
– volume: 15
  start-page: 12
  year: 2007
  ident: ref_209
  article-title: Finding the Direction of Disturbance Propagation in a Chemical Process Using Transfer Entropy
  publication-title: IEEE Trans. Control Syst. Technol.
  doi: 10.1109/TCST.2006.883234
– ident: ref_137
  doi: 10.3390/pr10010169
– ident: ref_127
  doi: 10.1109/DDCLS49620.2020.9275054
– volume: 44
  start-page: 73
  year: 2016
  ident: ref_25
  article-title: Kullback-Leibler distance-based enhanced detection of incipient anomalies
  publication-title: J. Loss Prev. Process Ind.
  doi: 10.1016/j.jlp.2016.08.020
– volume: 51
  start-page: 81
  year: 2000
  ident: ref_74
  article-title: Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/S0169-7439(00)00058-7
– volume: 68
  start-page: 506
  year: 2012
  ident: ref_180
  article-title: A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/j.ces.2011.10.011
– volume: 24
  start-page: 175
  year: 2000
  ident: ref_19
  article-title: Comparison of statistical process monitoring methods: Application to the Eastman challenge problem
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/S0098-1354(00)00509-3
– volume: 56
  start-page: 2054
  year: 2017
  ident: ref_203
  article-title: Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.6b01916
– volume: 145
  start-page: 114
  year: 2015
  ident: ref_123
  article-title: An Improved Detection Statistic for Monitoring the Nonstationary and Nonlinear Processes
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2015.04.016
– volume: 45
  start-page: 1593
  year: 2009
  ident: ref_195
  article-title: Reconstruction-based contribution for process monitoring
  publication-title: Automatica
  doi: 10.1016/j.automatica.2009.02.027
– volume: 30
  start-page: 179
  year: 1995
  ident: ref_72
  article-title: Disturbance detection and isolation by dynamic principal component analysis
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/0169-7439(95)00076-3
– volume: 53
  start-page: 230
  year: 2014
  ident: ref_210
  article-title: Process fault isolation based on transfer entropy algorithm
  publication-title: ISA Trans.
  doi: 10.1016/j.isatra.2013.11.007
– volume: 10
  start-page: 1299
  year: 1998
  ident: ref_89
  article-title: Nonlinear component analysis as a kernel eigenvalue problem
  publication-title: Neural Comput.
  doi: 10.1162/089976698300017467
– volume: 87
  start-page: 40
  year: 2009
  ident: ref_187
  article-title: SDG-based HAZOP analysis of operating mistakes for PVC process
  publication-title: Process Saf. Environ. Prot.
  doi: 10.1016/j.psep.2008.06.004
– volume: 14
  start-page: 467
  year: 2004
  ident: ref_43
  article-title: Statistical process monitoring with independent component analysis
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2003.09.004
– volume: 53
  start-page: 7696
  year: 2014
  ident: ref_71
  article-title: Process Monitoring with Global–Local Preserving Projections
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie4039345
– ident: ref_13
  doi: 10.3390/pr5030035
– volume: 118
  start-page: 287
  year: 2012
  ident: ref_152
  article-title: A novel local neighborhood standardization strategy and its application in fault detection of multimode processes
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2012.05.010
– volume: 130
  start-page: 135
  year: 2014
  ident: ref_200
  article-title: Reconstruction based fault diagnosis using concurrent phase partition and analysis of relative changes for multiphase batch processes with limited fault batches
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2013.10.014
– volume: 27
  start-page: 327
  year: 2003
  ident: ref_11
  article-title: A review of process fault detection and diagnosis Part III: Process history based methods
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/S0098-1354(02)00162-X
– volume: 216
  start-page: 1
  year: 2021
  ident: ref_75
  article-title: Dynamic Non-Gaussian hybrid serial modeling for industrial process monitoring
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2021.104371
– volume: 75
  start-page: 77
  year: 2019
  ident: ref_216
  article-title: Distributed partial least squares based residual generation for statistical process monitoring
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2019.01.005
– volume: 48
  start-page: 8585
  year: 2009
  ident: ref_181
  article-title: Multiway Gaussian mixture model based multiphase batch process monitoring
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie900479g
– volume: 95
  start-page: 68
  year: 2019
  ident: ref_76
  article-title: Complex dynamic process monitoring method based on slow feature analysis model of multi-subspace partitioning
  publication-title: ISA Trans.
  doi: 10.1016/j.isatra.2019.05.013
– volume: 26
  start-page: 17
  year: 2015
  ident: ref_192
  article-title: Canonical variate analysis-based contributions for fault identification
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2014.12.001
– volume: 29
  start-page: 1290
  year: 1990
  ident: ref_185
  article-title: On-line fault diagnosis using the signed directed graph
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie00103a031
– ident: ref_111
  doi: 10.7551/mitpress/1120.003.0080
– volume: 57
  start-page: 292
  year: 2017
  ident: ref_158
  article-title: Multimode Process Monitoring Approach Based on Moving Window Hidden Markov Model
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.7b03600
– volume: 28
  start-page: 409
  year: 2018
  ident: ref_191
  article-title: Multivariate SPC Methods for Process and Product Monitoring
  publication-title: J. Qual. Technol.
  doi: 10.1080/00224065.1996.11979699
– volume: 45
  start-page: 160
  year: 2012
  ident: ref_206
  article-title: Root cause diagnosis of plant-wide oscillations using granger causality
  publication-title: IFAC Proc. Vol.
  doi: 10.3182/20120710-4-SG-2026.00172
– volume: 58
  start-page: 131
  year: 2017
  ident: ref_41
  article-title: Fault detection of process correlation structure using canonical variate analysis-based correlation features
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2017.09.003
– volume: 50
  start-page: 2777
  year: 2014
  ident: ref_26
  article-title: Detecting abnormal situations using the Kullback–Leibler divergence
  publication-title: Automatica
  doi: 10.1016/j.automatica.2014.09.005
– volume: 8
  start-page: 775
  year: 1998
  ident: ref_190
  article-title: Contribution plots: A missing link in multivariate quality control
  publication-title: Appl. Math. Comput. Sci.
– volume: 50
  start-page: 29
  year: 2020
  ident: ref_78
  article-title: Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring
  publication-title: Annu. Rev. Control
  doi: 10.1016/j.arcontrol.2020.09.004
– ident: ref_68
  doi: 10.1109/ICDM.2008.17
– volume: 14
  start-page: 715
  year: 2002
  ident: ref_82
  article-title: Slow feature analysis: Unsupervised learning of invariances
  publication-title: Neural Comput.
  doi: 10.1162/089976602317318938
– volume: 142
  start-page: 106376
  year: 2020
  ident: ref_88
  article-title: A review of machine learning kernel methods in statistical process monitoring
  publication-title: Comput. Ind. Eng.
  doi: 10.1016/j.cie.2020.106376
– volume: 124
  start-page: 104580
  year: 2020
  ident: ref_1
  article-title: Analysis of severe industrial accidents caused by hazardous chemicals in South Korea from January 2008 to June 2018
  publication-title: Saf. Sci.
  doi: 10.1016/j.ssci.2019.104580
– volume: 69
  start-page: 35
  year: 1991
  ident: ref_36
  article-title: Multivariate statistical monitoring of process operating performance
  publication-title: Can. J. Chem. Eng.
  doi: 10.1002/cjce.5450690105
– volume: 96
  start-page: 687
  year: 2007
  ident: ref_144
  article-title: Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor
  publication-title: Biotechnol. Bioeng.
  doi: 10.1002/bit.21220
– volume: 52
  start-page: 9858
  year: 2013
  ident: ref_199
  article-title: Weighted Reconstruction-Based Contribution for Improved Fault Diagnosis
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie300679e
– ident: ref_120
  doi: 10.3390/pr10010122
– volume: 189
  start-page: 56
  year: 2019
  ident: ref_18
  article-title: Data-driven monitoring of multimode continuous processes: A review
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2019.03.012
– volume: 33
  start-page: 172
  year: 2009
  ident: ref_172
  article-title: A survey on multistage/multiphase statistical modeling methods for batch processes
  publication-title: Annu. Rev. Control
  doi: 10.1016/j.arcontrol.2009.08.001
– volume: 259
  start-page: 369
  year: 2014
  ident: ref_96
  article-title: Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2013.06.021
– volume: 21
  start-page: 627
  year: 2011
  ident: ref_183
  article-title: A method for multiphase batch process monitoring based on auto phase identification
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2010.12.003
– ident: ref_217
  doi: 10.1016/B978-0-12-823377-1.50195-6
– volume: 48
  start-page: 619
  year: 2015
  ident: ref_197
  article-title: Sparse contribution plot for fault diagnosis of multimodal chemical processes
  publication-title: IFAC-PapersOnLine
  doi: 10.1016/j.ifacol.2015.09.595
– volume: 22
  start-page: 205
  year: 2014
  ident: ref_97
  article-title: Online monitoring of nonlinear multivariate industrial processes using filtering KICA–PCA
  publication-title: Control Eng. Pract.
  doi: 10.1016/j.conengprac.2013.06.017
– volume: 64
  start-page: 62
  year: 2018
  ident: ref_66
  article-title: Weighted random forests for fault classification in industrial processes with hierarchical clustering model selection
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2018.02.005
– ident: ref_214
  doi: 10.1109/SYSTOL.2010.5676081
– volume: 65
  start-page: 466
  year: 2019
  ident: ref_55
  article-title: The promise of artificial intelligence in chemical engineering: Is it here, finally
  publication-title: AlChE J.
  doi: 10.1002/aic.16489
– volume: 49
  start-page: 7849
  year: 2010
  ident: ref_92
  article-title: Reconstruction-based contribution for process monitoring with kernel principal component analysis
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie9018947
– volume: 21
  start-page: 949
  year: 2011
  ident: ref_176
  article-title: Batch process monitoring based on support vector data description method
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2011.02.004
– volume: 126
  start-page: 465
  year: 2019
  ident: ref_16
  article-title: Advances and opportunities in machine learning for process data analytics
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2019.04.003
– volume: 42
  start-page: 1115
  year: 2009
  ident: ref_35
  article-title: Data-driven Fault Detection and Diagnosis for Complex Industrial Processes
  publication-title: IFAC Proc. Vol.
  doi: 10.3182/20090630-4-ES-2003.00184
– volume: 60
  start-page: 279
  year: 2005
  ident: ref_91
  article-title: Fault identification for process monitoring using kernel principal component analysis
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/j.ces.2004.08.007
– volume: 42
  start-page: 190
  year: 2016
  ident: ref_14
  article-title: Perspectives on process monitoring of industrial systems
  publication-title: Annu. Rev. Control
  doi: 10.1016/j.arcontrol.2016.09.001
– volume: 129
  start-page: 106515
  year: 2019
  ident: ref_27
  article-title: A novel process monitoring approach based on variational recurrent autoencoder
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2019.106515
– volume: 14
  start-page: 486
  year: 2006
  ident: ref_73
  article-title: Fault Isolation by Partial Dynamic Principal Component Analysis in Dynamic Process
  publication-title: Chin. J. Chem. Eng.
  doi: 10.1016/S1004-9541(06)60103-1
– volume: 98
  start-page: 1269
  year: 2020
  ident: ref_167
  article-title: Batch process monitoring using multiway Laplacian autoencoders
  publication-title: Can. J. Chem. Eng.
  doi: 10.1002/cjce.23738
– ident: ref_8
– volume: 95
  start-page: 10
  year: 2020
  ident: ref_85
  article-title: Enhanced canonical variate analysis with slow feature for dynamic process status analytics
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2020.09.005
– volume: 81
  start-page: 127
  year: 2006
  ident: ref_177
  article-title: Multi-phase principal component analysis for batch processes modelling
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2005.11.003
– volume: 51
  start-page: 482
  year: 2018
  ident: ref_151
  article-title: PCA-SDG based process monitoring and fault diagnosis: Application to an industrial pyrolysis furnace
  publication-title: IFAC-PapersOnLine
  doi: 10.1016/j.ifacol.2018.09.378
– volume: 47
  start-page: 1120
  year: 2008
  ident: ref_93
  article-title: Nonlinear multivariate quality estimation and prediction based on kernel partial least squares
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie070741+
– volume: 146
  start-page: 136
  year: 2015
  ident: ref_189
  article-title: Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO)
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2015.05.019
– ident: ref_2
  doi: 10.3390/su10082935
– volume: 83
  start-page: 13
  year: 2019
  ident: ref_109
  article-title: Process monitoring using variational autoencoder for high-dimensional nonlinear processes
  publication-title: Eng. Appl. Artif. Intell.
  doi: 10.1016/j.engappai.2019.04.013
– volume: 72
  start-page: 3
  year: 2010
  ident: ref_52
  article-title: Sparse partial least squares regression for simultaneous dimension reduction and variable selection
  publication-title: J. R. Stat. Soc. Ser. B
  doi: 10.1111/j.1467-9868.2009.00723.x
– volume: 50
  start-page: 6837
  year: 2011
  ident: ref_115
  article-title: Global–Local Structure Analysis Model and Its Application for Fault Detection and Identification
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie102564d
– volume: 47
  start-page: 121
  year: 2016
  ident: ref_118
  article-title: Improved fault detection and diagnosis using sparse global-local preserving projections
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2016.09.007
– volume: 56
  start-page: 10743
  year: 2017
  ident: ref_119
  article-title: Enhanced Fault Detection Based on Ensemble Global–Local Preserving Projections with Quantitative Global–Local Structure Analysis
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.7b01642
– volume: 25
  start-page: 686
  year: 2012
  ident: ref_4
  article-title: Characteristics of hazardous chemical accidents in China: A statistical investigation
  publication-title: J. Loss Prev. Process Ind.
  doi: 10.1016/j.jlp.2012.03.001
– volume: 31
  start-page: 105779
  year: 2020
  ident: ref_126
  article-title: ARIMA modelling and forecasting of irregularly patterned COVID-19 outbreaks using Japanese and South Korean data
  publication-title: Data Brief
  doi: 10.1016/j.dib.2020.105779
– volume: 290
  start-page: 2323
  year: 2000
  ident: ref_70
  article-title: Nonlinear dimensionality reduction by locally linear embedding
  publication-title: Science
  doi: 10.1126/science.290.5500.2323
– volume: 32
  start-page: 109
  year: 2015
  ident: ref_40
  article-title: Canonical variate analysis-based monitoring of process correlation structure using causal feature representation
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2015.05.004
– volume: 157
  start-page: 107587
  year: 2022
  ident: ref_81
  article-title: Retrospective comparison of several typical linear dynamic latent variable models for industrial process monitoring
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2021.107587
– volume: 115
  start-page: 185
  year: 2018
  ident: ref_56
  article-title: Deep convolutional neural network model based chemical process fault diagnosis
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2018.04.009
– volume: 16
  start-page: 153
  year: 2004
  ident: ref_112
  article-title: Locality preserving projections
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 2
  start-page: 1
  year: 2015
  ident: ref_106
  article-title: Variational autoencoder based anomaly detection using reconstruction probability
  publication-title: Spec. Lect. IE
– volume: 51
  start-page: 374
  year: 2011
  ident: ref_145
  article-title: Multimode Process Monitoring Based on Mode Identification
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie102048f
– volume: 185
  start-page: 110064
  year: 2021
  ident: ref_69
  article-title: A new method for fault detection of aero-engine based on isolation forest
  publication-title: Measurement
  doi: 10.1016/j.measurement.2021.110064
– volume: 81
  start-page: 541
  year: 2020
  ident: ref_188
  article-title: Fault Diagnosis Algorithm of Chemical Process Based on Information Entropy
  publication-title: Chem. Eng. Trans.
– volume: 35
  start-page: 342
  year: 2011
  ident: ref_28
  article-title: Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2010.05.004
– volume: 40
  start-page: 4403
  year: 2001
  ident: ref_30
  article-title: Reconstruction-based fault identification using a combined index
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie000141+
– volume: 65
  start-page: 265
  year: 2016
  ident: ref_125
  article-title: Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model
  publication-title: Microelectron. Reliab.
  doi: 10.1016/j.microrel.2016.07.151
– volume: 106
  start-page: 1
  year: 2017
  ident: ref_215
  article-title: Refined convergent cross-mapping for disturbance propagation analysis of chemical processes
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2017.03.026
– volume: 46
  start-page: 7780
  year: 2007
  ident: ref_166
  article-title: Fault detection of nonlinear processes using multiway kernel independent component analysis
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie070381q
– volume: 7
  start-page: 903
  year: 1999
  ident: ref_186
  article-title: PCA-SDG based process monitoring and fault diagnosis
  publication-title: Control. Eng. Pract.
  doi: 10.1016/S0967-0661(99)00040-4
– volume: 44
  start-page: 12886
  year: 2011
  ident: ref_79
  article-title: Dynamic latent variable modeling for statistical process monitoring
  publication-title: IFAC Proc. Vol.
  doi: 10.3182/20110828-6-IT-1002.00934
– volume: 16
  start-page: 1021
  year: 2006
  ident: ref_178
  article-title: Online monitoring of batch processes using multi-phase principal component analysis
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2006.07.005
– volume: 44
  start-page: 331
  year: 1998
  ident: ref_162
  article-title: Modelling and diagnostics of batch processes and analogous kinetic experiments
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/S0169-7439(98)00162-2
– volume: 27
  start-page: 313
  year: 2003
  ident: ref_10
  article-title: A review of process fault detection and diagnosis Part II: Qualitative models and search strategies
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/S0098-1354(02)00161-8
– volume: 65
  start-page: 8895
  year: 2018
  ident: ref_133
  article-title: Recursive Slow Feature Analysis for Adaptive Monitoring of Industrial Processes
  publication-title: IEEE Trans. Ind. Electron.
  doi: 10.1109/TIE.2018.2811358
– volume: 65
  start-page: 5961
  year: 2010
  ident: ref_154
  article-title: Statistical analysis and online monitoring for multimode processes with between-mode transitions
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/j.ces.2010.08.024
– volume: 25
  start-page: 1103
  year: 2001
  ident: ref_22
  article-title: A new multivariate statistical process monitoring method using principal component analysis
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/S0098-1354(01)00683-4
– volume: 50
  start-page: 13898
  year: 2017
  ident: ref_208
  article-title: Root cause diagnosis of oscillation-type plant faults using nonlinear causality analysis
  publication-title: IFAC-PapersOnLine
  doi: 10.1016/j.ifacol.2017.08.2208
– volume: 71
  start-page: 90
  year: 2018
  ident: ref_53
  article-title: Sparse canonical variate analysis approach for process monitoring
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2018.09.009
– volume: 7
  start-page: 173827
  year: 2019
  ident: ref_101
  article-title: Outlier Detection for Monitoring Data Using Stacked Autoencoder
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2956494
– volume: 148
  start-page: 51
  year: 2015
  ident: ref_157
  article-title: Hidden Markov model-based approach for multimode process monitoring
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2015.08.025
– volume: 135
  start-page: 76
  year: 2014
  ident: ref_94
  article-title: New contributions to non-linear process monitoring through kernel partial least squares
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2014.04.001
– volume: 47
  start-page: 10616
  year: 2014
  ident: ref_122
  article-title: Nonstationarity and cointegration tests for fault detection of dynamic processes
  publication-title: IFAC Proc. Vol.
  doi: 10.3182/20140824-6-ZA-1003.00754
– volume: 53
  start-page: 994
  year: 2020
  ident: ref_169
  article-title: Multiway dynamic nonlinear global neighborhood preserving embedding method for monitoring batch process
  publication-title: Meas. Control
  doi: 10.1177/0020294020911390
– volume: 59
  start-page: 5897
  year: 2004
  ident: ref_31
  article-title: Nonlinear dynamic process monitoring based on dynamic kernel PCA
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/j.ces.2004.07.019
– volume: 171
  start-page: 16
  year: 2017
  ident: ref_15
  article-title: Review on data-driven modeling and monitoring for plant-wide industrial processes
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2017.09.021
– volume: 107
  start-page: 104692
  year: 2021
  ident: ref_98
  article-title: Key-performance-indicator-related state monitoring based on kernel canonical correlation analysis
  publication-title: Control Eng. Pract.
  doi: 10.1016/j.conengprac.2020.104692
– volume: 64
  start-page: 1662
  year: 2017
  ident: ref_138
  article-title: A full-condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis
  publication-title: AlChE J.
  doi: 10.1002/aic.16048
– volume: 7
  start-page: 891
  year: 1999
  ident: ref_17
  article-title: Real-time monitoring for a process with multiple operating modes
  publication-title: Control Eng. Pract.
  doi: 10.1016/S0967-0661(99)00038-6
– ident: ref_59
  doi: 10.1145/2689746.2689747
– volume: 39
  start-page: 88
  year: 2016
  ident: ref_99
  article-title: Related and independent variable fault detection based on KPCA and SVDD
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2016.01.001
– volume: 98
  start-page: 1280
  year: 2020
  ident: ref_57
  article-title: A process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network
  publication-title: Can. J. Chem. Eng.
  doi: 10.1002/cjce.23740
– volume: 110
  start-page: 144
  year: 2012
  ident: ref_155
  article-title: Process monitoring based on mode identification for multi-mode process with transitions
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2011.10.013
– volume: 27
  start-page: 130
  year: 2015
  ident: ref_63
  article-title: Decision tree methods: Applications for classification and prediction
  publication-title: Shanghai Arch. Psychiatry
– volume: 135
  start-page: 106762
  year: 2020
  ident: ref_121
  article-title: Extracting nonstationary features for process data analytics and application in fouling detection
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2020.106762
– volume: 48
  start-page: 9163
  year: 2009
  ident: ref_179
  article-title: Nonlinear Batch Process Monitoring Using Phase-Based Kernel-Independent Component AnalysisPrincipal Component Analysis (KICAPCA)
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie8012874
– volume: 141
  start-page: 106978
  year: 2020
  ident: ref_6
  article-title: Forty years of computers & chemical engineering: A bibliometric analysis
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2020.106978
– volume: 64
  start-page: 49
  year: 2018
  ident: ref_105
  article-title: Automated feature learning for nonlinear process monitoring—An approach using stacked denoising autoencoder and k-neare.est neighbor rule
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2018.02.004
– volume: 42
  start-page: 107
  year: 2009
  ident: ref_62
  article-title: One-class classification-based control charts for multivariate process monitoring
  publication-title: IIE Trans.
  doi: 10.1080/07408170903019150
– volume: 83
  start-page: 63
  year: 2019
  ident: ref_33
  article-title: Mutual information-based sparse multiblock dissimilarity method for incipient fault detection and diagnosis in plant-wide process
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2019.09.004
– volume: 49
  start-page: 693
  year: 2016
  ident: ref_50
  article-title: Use of sparse principal component analysis (SPCA) for fault detection
  publication-title: IFAC-PapersOnLine
  doi: 10.1016/j.ifacol.2016.07.259
– volume: 48
  start-page: 3533
  year: 2009
  ident: ref_135
  article-title: Cointegration testing method for monitoring nonstationary processes
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie801611s
– volume: 36
  start-page: 220
  year: 2012
  ident: ref_12
  article-title: Survey on data-driven industrial process monitoring and diagnosis
  publication-title: Annu. Rev. Control
  doi: 10.1016/j.arcontrol.2012.09.004
– volume: 54
  start-page: 45
  year: 2004
  ident: ref_61
  article-title: Support vector data description
  publication-title: Mach. Learn.
  doi: 10.1023/B:MACH.0000008084.60811.49
– volume: 51
  start-page: 95
  year: 2000
  ident: ref_194
  article-title: Generalized contribution plots in multivariate statistical process monitoring
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/S0169-7439(00)00062-9
– volume: 59
  start-page: 223
  year: 2004
  ident: ref_90
  article-title: Nonlinear process monitoring using kernel principal component analysis
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/j.ces.2003.09.012
– volume: 18
  start-page: 707
  year: 2008
  ident: ref_205
  article-title: A practical method for identifying the propagation path of plant-wide disturbances
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2007.11.007
– volume: 105
  start-page: 27
  year: 2021
  ident: ref_86
  article-title: Dynamic system modelling and process monitoring based on long-term dependency slow feature analysis
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2021.07.007
– volume: 51
  start-page: 5497
  year: 2012
  ident: ref_149
  article-title: Dynamic Multimode Process Modeling and Monitoring Using Adaptive Gaussian Mixture Models
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie202720y
– volume: 67
  start-page: 112
  year: 2018
  ident: ref_49
  article-title: Real-time fault detection and diagnosis using sparse principal component analysis
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2017.03.005
– volume: 11
  start-page: 613
  year: 2003
  ident: ref_132
  article-title: Recursive partial least squares algorithms for monitoring complex industrial processes
  publication-title: Control Eng. Pract.
  doi: 10.1016/S0967-0661(02)00096-5
– volume: 17
  start-page: 245
  year: 1993
  ident: ref_34
  article-title: A plant-wide industrial process control problem
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/0098-1354(93)80018-I
– volume: 48
  start-page: 583
  year: 2015
  ident: ref_67
  article-title: Fault detection using random forest similarity distance
  publication-title: IFAC-PapersOnLine
  doi: 10.1016/j.ifacol.2015.09.589
– volume: 189
  start-page: 191
  year: 2018
  ident: ref_202
  article-title: Process system fault detection and diagnosis using a hybrid technique
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/j.ces.2018.05.045
– volume: 48
  start-page: 1231
  year: 2002
  ident: ref_24
  article-title: Statistical process monitoring based on dissimilarity of process data
  publication-title: AlChE J.
  doi: 10.1002/aic.690480610
– volume: 49
  start-page: 969
  year: 2003
  ident: ref_42
  article-title: Monitoring independent components for fault detection
  publication-title: AlChE J.
  doi: 10.1002/aic.690490414
– volume: 85
  start-page: 526
  year: 2007
  ident: ref_95
  article-title: Fault detection of non-linear processes using kernel independent component analysis
  publication-title: Can. J. Chem. Eng.
  doi: 10.1002/cjce.5450850414
– volume: 16
  start-page: 763
  year: 2006
  ident: ref_143
  article-title: Performance monitoring of processes with multiple operating modes through multiple PLS models
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2005.12.002
– volume: 84
  start-page: 139
  year: 2019
  ident: ref_140
  article-title: Monitoring nonstationary and dynamic trends for practical process fault diagnosis
  publication-title: Control Eng. Pract.
  doi: 10.1016/j.conengprac.2018.11.020
– volume: 55
  start-page: 4613
  year: 2016
  ident: ref_156
  article-title: Hidden Markov Model-Based Fault Detection Approach for a Multimode Process
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.5b04777
– volume: 20
  start-page: 273
  year: 1995
  ident: ref_60
  article-title: Support-vector networks
  publication-title: Mach. Learn.
  doi: 10.1007/BF00994018
– volume: 56
  start-page: 8895
  year: 2017
  ident: ref_139
  article-title: Monitoring Nonstationary Dynamic Systems Using Cointegration and Common-Trends Analysis
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.7b00011
– volume: 106
  start-page: 96
  year: 2017
  ident: ref_146
  article-title: Multi-mode operation of principal component analysis with k-nearest neighbor algorithm to monitor compressors for liquefied natural gas mixed refrigerant processes
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2017.05.029
– volume: 22
  start-page: 503
  year: 1998
  ident: ref_131
  article-title: Recursive PLS algorithms for adaptive data modeling
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/S0098-1354(97)00262-7
– volume: 210
  start-page: 104230
  year: 2021
  ident: ref_150
  article-title: Multimodal process monitoring based on variational Bayesian PCA and Kullback-Leibler divergence between mixture models
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2020.104230
– volume: 171
  start-page: 108782
  year: 2021
  ident: ref_104
  article-title: Nonlinear process monitoring based on load weighted denoising autoencoder
  publication-title: Measurement
  doi: 10.1016/j.measurement.2020.108782
– volume: 44
  start-page: 5691
  year: 2005
  ident: ref_129
  article-title: Process monitoring approach using fast moving window PCA
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie048873f
– volume: 37
  start-page: 41
  year: 1995
  ident: ref_173
  article-title: Multivariate SPC charts for monitoring batch processes
  publication-title: Technometrics
  doi: 10.1080/00401706.1995.10485888
– volume: 42
  start-page: 995
  year: 1996
  ident: ref_29
  article-title: Statistical process monitoring and disturbance diagnosis in multivariable continuous processes
  publication-title: AlChE J.
  doi: 10.1002/aic.690420412
– volume: 32
  start-page: 6686
  year: 1999
  ident: ref_128
  article-title: Recursive PCA for Adaptive Process Monitoring
  publication-title: IFAC Proc. Vol.
  doi: 10.1016/S1474-6670(17)57142-6
– ident: ref_58
  doi: 10.1109/INDIN.2017.8104910
– volume: 37
  start-page: 46
  year: 2016
  ident: ref_114
  article-title: A novel process monitoring and fault detection approach based on statistics locality preserving projections
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2015.11.004
– volume: 19
  start-page: 816
  year: 2009
  ident: ref_182
  article-title: Phase and transition based batch process modeling and online monitoring
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2008.11.001
– volume: 156
  start-page: 581
  year: 2021
  ident: ref_110
  article-title: A novel orthogonal self-attentive variational autoencoder method for interpretable chemical process fault detection and identification
  publication-title: Process Saf. Environ. Prot.
  doi: 10.1016/j.psep.2021.10.036
– volume: 14
  start-page: 725
  year: 2000
  ident: ref_193
  article-title: Confidence limits for contribution plots
  publication-title: J. Chemom. A J. Chemom. Soc.
– volume: 50
  start-page: 255
  year: 2004
  ident: ref_175
  article-title: Sub-PCA modeling and on-line monitoring strategy for batch processes
  publication-title: AlChE J.
  doi: 10.1002/aic.10024
– volume: 97
  start-page: 052216
  year: 2018
  ident: ref_207
  article-title: Causal inference in nonlinear systems: Granger causality versus time-delayed mutual information
  publication-title: Phys. Rev. E
  doi: 10.1103/PhysRevE.97.052216
– volume: 169
  start-page: 1
  year: 2017
  ident: ref_84
  article-title: Slow feature analysis based on online feature reordering and feature selection for dynamic chemical process monitoring
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2017.07.013
– volume: 195
  start-page: 777
  year: 2019
  ident: ref_204
  article-title: Fault detection and pathway analysis using a dynamic Bayesian network
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/j.ces.2018.10.024
– volume: 40
  start-page: 1361
  year: 1994
  ident: ref_159
  article-title: Monitoring batch processes using multiway principal component analysis
  publication-title: AlChE J.
  doi: 10.1002/aic.690400809
– volume: 43
  start-page: 2002
  year: 1997
  ident: ref_39
  article-title: Statistical monitoring of multivariable dynamic processes with state-space models
  publication-title: AlChE J.
  doi: 10.1002/aic.690430810
– volume: 28
  start-page: 1837
  year: 2004
  ident: ref_165
  article-title: Fault detection of batch processes using multiway kernel principal component analysis
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2004.02.036
– ident: ref_102
  doi: 10.1145/1390156.1390294
– ident: ref_213
  doi: 10.3390/pr9061027
– volume: 24
  start-page: 88
  year: 2018
  ident: ref_20
  article-title: Multivariate Control Charts for Individual Observations
  publication-title: J. Qual. Technol.
  doi: 10.1080/00224065.1992.12015232
– volume: 57
  start-page: 63
  year: 2002
  ident: ref_163
  article-title: On-line batch process monitoring using dynamic PCA and dynamic PLS models
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/S0009-2509(01)00366-9
– volume: 75
  start-page: 136
  year: 2019
  ident: ref_108
  article-title: Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2019.01.008
– volume: 30
  start-page: 97
  year: 1995
  ident: ref_161
  article-title: Multi-way partial least squares in monitoring batch processes
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/0169-7439(95)00043-7
– volume: 142
  start-page: 107064
  year: 2020
  ident: ref_212
  article-title: Data-driven fault diagnosis for chemical processes using transfer entropy and graphical lasso
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2020.107064
– volume: 28
  start-page: 1377
  year: 2004
  ident: ref_44
  article-title: Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2003.09.031
– volume: 52
  start-page: 3543
  year: 2013
  ident: ref_5
  article-title: Review of Recent Research on Data-Based Process Monitoring
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie302069q
– volume: 49
  start-page: 140
  year: 2003
  ident: ref_38
  article-title: Detecting abnormal process trends by wavelet-domain hidden Markov models
  publication-title: AlChE J.
  doi: 10.1002/aic.690490113
– volume: 13
  start-page: 2227
  year: 2017
  ident: ref_201
  article-title: Bayesian Networks in Fault Diagnosis
  publication-title: IEEE Trans. Ind. Inf.
  doi: 10.1109/TII.2017.2695583
– volume: 159
  start-page: 1
  year: 2016
  ident: ref_211
  article-title: Data-driven root cause diagnosis of faults in process industries
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2016.09.006
– ident: ref_107
– volume: 44
  start-page: 1596
  year: 1998
  ident: ref_37
  article-title: Multiscale PCA with application to multivariate statistical process monitoring
  publication-title: AlChE J.
  doi: 10.1002/aic.690440712
– volume: 85
  start-page: 119
  year: 2019
  ident: ref_77
  article-title: Fault detection in dynamic plant-wide process by multi-block slow feature analysis and support vector data description
  publication-title: ISA Trans.
  doi: 10.1016/j.isatra.2018.10.016
– ident: ref_124
– volume: 58
  start-page: 34
  year: 2017
  ident: ref_32
  article-title: Ensemble modified independent component analysis for enhanced non-Gaussian process monitoring
  publication-title: Control Eng. Pract.
  doi: 10.1016/j.conengprac.2016.09.014
– volume: 141
  start-page: 107024
  year: 2020
  ident: ref_141
  article-title: Self-adaptive deep learning for multimode process monitoring
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2020.107024
– volume: 149
  start-page: 312
  year: 2021
  ident: ref_64
  article-title: Decision trees for informative process alarm definition and alarm-based fault classification
  publication-title: Process Saf. Environ. Prot.
  doi: 10.1016/j.psep.2020.10.024
– volume: 54
  start-page: 11866
  year: 2015
  ident: ref_148
  article-title: Novel Monitoring Strategy Combining the Advantages of the Multiple Modeling Strategy and Gaussian Mixture Model for Multimode Processes
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.5b00373
– volume: 40
  start-page: 24
  year: 2016
  ident: ref_198
  article-title: On the use of reconstruction-based contribution for fault diagnosis
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2016.01.011
– volume: 58
  start-page: 12899
  year: 2019
  ident: ref_87
  article-title: Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.9b02391
– volume: 128
  start-page: 104741
  year: 2020
  ident: ref_3
  article-title: Chemical industry in China: The current status, safety problems, and pathways for future sustainable development
  publication-title: Saf. Sci.
  doi: 10.1016/j.ssci.2020.104741
– volume: 29
  start-page: 126
  year: 2015
  ident: ref_153
  article-title: A novel multi-mode data processing method and its application in industrial process monitoring
  publication-title: J. Chemom.
  doi: 10.1002/cem.2686
– volume: 92
  start-page: 319
  year: 2020
  ident: ref_136
  article-title: Recursive cointegration analytics for adaptive monitoring of nonstationary industrial processes with both static and dynamic variations
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2020.06.013
– volume: 21
  start-page: 341
  year: 1979
  ident: ref_21
  article-title: Control Procedures for Residuals Associated With Principal Component Analysis
  publication-title: Technometrics
  doi: 10.1080/00401706.1979.10489779
– volume: 15
  start-page: 265
  year: 2006
  ident: ref_51
  article-title: Sparse Principal Component Analysis
  publication-title: J. Comput. Graph. Stat.
  doi: 10.1198/106186006X113430
– volume: 55
  start-page: 251
  year: 1987
  ident: ref_134
  article-title: Co-integration and error correction: Representation, estimation, and testing
  publication-title: Econom. J. Econom. Soc.
– volume: 313
  start-page: 504
  year: 2006
  ident: ref_100
  article-title: Reducing the dimensionality of data with neural networks
  publication-title: Science
  doi: 10.1126/science.1127647
– volume: 138
  start-page: 178
  year: 2014
  ident: ref_174
  article-title: Inter-batch-evolution-traced process monitoring based on inter-batch mode division for multiphase batch processes
  publication-title: Chemom. Intell. Lab. Syst.
  doi: 10.1016/j.chemolab.2014.08.007
– volume: 22
  start-page: 1358
  year: 2012
  ident: ref_116
  article-title: Local and global principal component analysis for process monitoring
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2012.06.008
– volume: 57
  start-page: 3979
  year: 2002
  ident: ref_170
  article-title: Critical evaluation of approaches for on-line batch process monitoring
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/S0009-2509(02)00338-X
– volume: 43
  start-page: 7025
  year: 2004
  ident: ref_142
  article-title: Monitoring of processes with multiple operating modes through multiple principle component analysis models
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie0497893
– volume: 27
  start-page: 293
  year: 2003
  ident: ref_9
  article-title: A review of process fault detection and diagnosis Part I: Quantitative model-based methods
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/S0098-1354(02)00160-6
– volume: 54
  start-page: 1811
  year: 2008
  ident: ref_45
  article-title: Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models
  publication-title: AlChE J.
  doi: 10.1002/aic.11515
– volume: 22
  start-page: 40
  year: 2002
  ident: ref_171
  article-title: Statistical monitoring of multistage, multiphase batch processes
  publication-title: IEEE Control Syst. Mag.
  doi: 10.1109/MCS.2002.1035216
– volume: 21
  start-page: 360
  year: 2013
  ident: ref_196
  article-title: Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process
  publication-title: Control Eng. Pract.
  doi: 10.1016/j.conengprac.2012.11.013
– volume: 61
  start-page: 3666
  year: 2015
  ident: ref_83
  article-title: Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis
  publication-title: AlChE J.
  doi: 10.1002/aic.14888
– volume: 45
  start-page: 39
  year: 2012
  ident: ref_48
  article-title: A comprehensive evaluation of Statistics Pattern Analysis based process monitoring
  publication-title: IFAC Proc. Vol.
  doi: 10.3182/20120710-4-SG-2026.00033
– ident: ref_54
  doi: 10.1007/978-1-4471-5185-2
– volume: 26
  start-page: 161
  year: 2002
  ident: ref_23
  article-title: Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/S0098-1354(01)00738-4
– volume: 28
  start-page: 1083
  year: 2020
  ident: ref_103
  article-title: Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net
  publication-title: IEEE Trans. Control Syst. Technol.
  doi: 10.1109/TCST.2019.2897946
– volume: 23
  start-page: 1497
  year: 2013
  ident: ref_147
  article-title: An adaptive multimode process monitoring strategy based on mode clustering and mode unfolding
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2013.09.017
– volume: 5
  start-page: 2696
  year: 2017
  ident: ref_168
  article-title: Batch Process Monitoring Based on Multiway Global Preserving Kernel Slow Feature Analysis
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2017.2672780
– volume: 147
  start-page: 107252
  year: 2021
  ident: ref_7
  article-title: Process systems engineering—The generation next?
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2021.107252
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Snippet Safe and stable operation plays an important role in the chemical industry. Fault detection and diagnosis (FDD) make it possible to identify abnormal process...
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SubjectTerms Artificial intelligence
Carbon
Chemical industry
Data analysis
Data processing
Datasets
Distributed control systems
False alarms
Fault detection
Fault diagnosis
Industrial applications
Monitoring
Reviews
Simulation
Title A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data
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