[1]〖ZK(#〗张成,郭青秀,李元,等.基于主元分析得分重构差分的故障检测策略[J].控制理论与应用,2019,36(5):774-782.
[2]GAO Z W,DING S X,CECATI C.Real-Time Fault Diagnosis and Fault-Tolerant Control [J].IEEE Transactions on Industrial Electronics,2015,62(6):3752-3756.
[3]WANG G Z,LIU J C,ZHANG Y W,et al.A Novel Multi-Mode Data Processing Method and Its Application in Industrial Process Monitoring [J].Journal of Chemometrics,2015,29(2):126-138.
[4]郭金玉,刘玉超,李元.基于概率密度PCA的多模态过程故障检测[J].计算机应用研究,2019,36(5):1396-1399,1408.
[5]QIN S J.Process Data Analytics in the Era of Big Data [J].AIChE Journal,2014,60(9):3092-3100.
[6]郭小萍,刘诗洋,李元.基于稀疏残差距离的多工况过程故障检测方法研究[J].自动化学报,2019,45(3):617-625.
[7]YOO Y J.Fault Detection Method Using Multi-Mode Principal Component Analysis Based on Gaussian Mixture Model for Sewage Source Heat Pump System [J].International Journal of Control,Automation and Systems,2019,17(8):2125-2134.
[8]KU W F,STORER R H,GEORGAKIS C.Disturbance Detection and Isolation by Dynamic Principal Component Analysis[J].Chemometrics and Intelligent Laboratory Systems,1995,30(1):179-196.
[9]ZHAO S J,ZHANG J,XU Y M.Monitoring of Processes with Multiple Operating Modes Through Multiple Principle Component Analysis Models [J].Industrial & Engineering Chemistry Research,2004,43(22):7025-7035.
[10]ZHANG M G,GE Z Q,SONG Z H,et al.Global-Local Structure Analysis Model and Its Application for Fault Detection and Identification[J].Industrial & Engineering Chemistry Research,2011,50(11):6837-6848.
[11]LEWANDOWSKI M,MAKRIS D,VELASTIN S A,et al.Structural Laplacian Eigenmaps for Modeling Sets of Multivariate Sequences [J].IEEE Transactions on Cybernetics,2014,44(6):936-949.
[12]YAO B B,SU J,WANG L F,et al.Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis [J].Applied Sciences,2017,7(11):1178.
[13]HE X F,NIYOGI P.Locality Preserving Projections [C]//Proceedings of the 16th International Conference on Neural Information Processing Systems.Cambridge:MIT Press,2003:153-160.
[14]HE F,XU J W.A Novel Process Monitoring and Fault Detection Approach Based on Statistics Locality Preserving Projections [J].Journal of Process Control,2016,37:46-57.
[15]CAI L F,TIAN X M,ZHANG Y X.Dynamic Process Monitoring Based on Orthogonal Locality Preserving Projections and Exponentially Weighted Moving Average[C]//2013 25th Chinese Control and Decision Conference(CCDC).Guiyang:IEEE,2013:4337-4342.
[16]刘帮莉,马玉鑫,侍洪波.基于局部密度估计的多模态过程故障检测[J].化工学报,2014,65(8):3071-3081.
[17]张成,郭青秀,冯立伟,等.基于局部保持投影-加权k近邻规则的多模态间歇过程故障检测策略[J].控制理论与应用,2019,36(10):1682-1689.
[18]HE Q P,WANG J.Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes [J].IEEE Transactions on Semiconductor Manufacturing,2007,20(4):345-354.
[19]郭小萍,李婷,李元.基于LPP-kNN方法的间歇过程故障监视[J].沈阳化工大学学报,2017,31(3):261-265.
[20]郭金玉,刘玉超,李元.基于局部相对概率密度kNN的多模态过程故障检测[J].高校化学工程学报,2019,33(1):159-166.
[21]PENG K X,ZHANG K,YOU B,et al.A Quality-Based Nonlinear Fault Diagnosis Framework Focusing on Industrial Multimode Batch Processes [J].IEEE Transactions on Industrial Electronics,2016,63(4):2615-2624.
[22]CHOI S W,PARK J H,LEE I B.Process Monitoring Using a Gaussian Mixture Model via Principal Component Analysis and Discriminant Analysis [J].Computers & Chemical Engineering,2004,28(8):1377-1387.
[23]BISHOP C M.Pattern Recognition and Machine Learning(Information Science and Statistics)[M].Berlin:Springer-Verlag,2006:474-485.
[24]NASIOS N,BORS A G.Variational Learning for Gaussian Mixture Models [J].IEEE Transactions on Systems,Man,and Cybernetics.Part B,Cybernetics:2006,36(4):849-862.
[25]李元,杨东昇,赵丽颖,等.层次变分高斯混合模型与主多项式分析的故障检测策略[J].化工学报,2021,72(3):1616-1626.
[26]LIU J L,CHEN D S.Operational Performance Assessment and Fault Isolation for Multimode Processes [J].Industrial & Engineering Chemistry Research,2010,49(8):3700-3714.
[27]WISE B M,GALLAGHER N B,BUTLER S W,et al.A Comparison of Principal Component Analysis,Multiway Principal Component Analysis,Trilinear Decomposition and Parallel Factor Analysis for Fault Detection in a Semiconductor Etch Process[J].Journal of Chemometrics,1999,13(3/4):379-396.
[28]HE Q P,WANG J.Statistics Pattern Analysis:a New Process Monitoring Framework and Its Application to Semiconductor Batch Processes [J].AIChE Journal,2011,57(1):107-121.
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