Laplacian regularized robust principal component analysis for process monitoring
Tài liệu tham khảo
Ding, 2014
Liu, 2015, Adaptive sparse principal component analysis for enhanced process monitoring and fault isolation, Chemometr. Intell. Lab. Syst., 146, 426, 10.1016/j.chemolab.2015.06.014
Liu, 2018, Structured joint sparse principal component analysis for fault detection and isolation, IEEE Trans. Ind. Inf., 15, 2721, 10.1109/TII.2018.2868364
Xie, 2013, Shrinking principal component analysis for enhanced process monitoring and fault isolation, Ind. Eng. Chem. Res., 52, 17475, 10.1021/ie401030t
Zhu, 2017, Distributed parallel PCA for modeling and monitoring of large-scale plant-wide processes with big data, IEEE Trans. Ind. Inf., 13, 1877, 10.1109/TII.2017.2658732
Kruger, 2008, Robust partial least squares regression: Part i, algorithmic developments, J. Chemometr.: J. Chemometr. Soc., 22, 1, 10.1002/cem.1093
Peng, 2016, A quality-based nonlinear fault diagnosis framework focusing on industrial multimode batch processes, IEEE Trans. Ind. Electron., 63, 2615
Jiang, 2015, CaNonical variate analysis-based contributions for fault identification, J. Process Control, 26, 17, 10.1016/j.jprocont.2014.12.001
Chen, 2017, Fault detection for non-gaussian processes using generalized canonical correlation analysis and randomized algorithms, IEEE Trans. Ind. Electron., 65, 1559, 10.1109/TIE.2017.2733501
Hu, 2019, A sparse fault degradation oriented fisher discriminant analysis (FDFDA) algorithm for faulty variable isolation and its industrial application, Control Eng. Pract., 90, 311, 10.1016/j.conengprac.2019.07.007
Shang, 2016, Slow feature analysis for monitoring and diagnosis of control performance, J. Process Control, 39, 21, 10.1016/j.jprocont.2015.12.004
Yu, 2018, Recursive exponential slow feature analysis for fine-scale adaptive processes monitoring with comprehensive operation status identification, IEEE Trans. Ind. Inf., 15, 3311, 10.1109/TII.2018.2878405
Chai, 2020, Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification, IEEE Trans. Ind. Inf., 16, 54, 10.1109/TII.2019.2915559
Ge, 2017, Dynamic probabilistic latent variable model for process data modeling and regression application, IEEE Trans. Control Syst. Technol., 27, 323, 10.1109/TCST.2017.2767022
Raveendran, 2018, Process monitoring using a generalized probabilistic linear latent variable model, Automatica, 96, 73, 10.1016/j.automatica.2018.06.029
Martin, 1979
Chiang, 2000
Shang, 2018, Isolating incipient sensor fault based on recursive transformed component statistical analysis, J. Process Control, 64, 112, 10.1016/j.jprocont.2018.01.002
Cai, 2017, Bayesian networks in fault diagnosis, IEEE Trans. Ind. Inf., 13, 2227, 10.1109/TII.2017.2695583
Jiang, 2011, Anomaly localization for network data streams with graph joint sparse PCA, 886
Liu, 2017, Compressive sparse principal component analysis for process supervisory monitoring and fault detection, J. Process Control, 50, 1, 10.1016/j.jprocont.2016.11.010
Yan, 2015, Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO), Chemometr. Intell. Lab. Syst., 146, 136, 10.1016/j.chemolab.2015.05.019
Zou, 2006, Sparse principal component analysis, J. Comput. Graph. Statist., 15, 265, 10.1198/106186006X113430
Yi, 2017, Joint sparse principal component analysis, Pattern Recognit., 61, 524, 10.1016/j.patcog.2016.08.025
Li, 2019, Deep manifold structure transfer for action recognition, IEEE Trans. Image Process., 28, 4646, 10.1109/TIP.2019.2912357
Hubert, 2005, ROBPCA: a new approach to robust principal component analysis, Technometrics, 47, 64, 10.1198/004017004000000563
Stahel, 1981
Donoho, 1982
Hubert, 2016, Sparse PCA for high-dimensional data with outliers, Technometrics, 58, 424, 10.1080/00401706.2015.1093962
Candès, 2011, Robust principal component analysis?, J. ACM, 58, 1, 10.1145/1970392.1970395
Recht, 2010, Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization, SIAM Rev., 52, 471, 10.1137/070697835
Bouwmans, 2017, Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset, Comp. Sci. Rev., 23, 1, 10.1016/j.cosrev.2016.11.001
Yan, 2016, Robust multivariate statistical process monitoring via stable principal component pursuit, Ind. Eng. Chem. Res., 55, 4011, 10.1021/acs.iecr.5b02913
Pearson, 1901, Liii. On lines and planes of closest fit to systems of points in space, Lond. Edinb. Dublin Philos. Mag. J. Sci., 2, 559, 10.1080/14786440109462720
Liu, 2009, Multi-task feature learning via efficient ℓ2,1-norm minimization, 339
Zhou, 2007, Learning with hypergraphs: Clustering, classification, and embedding, Adv. Neural Inf. Process. Syst., 1601
Boyd, 2011, Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends® Mach. Learn., 3, 1
Donoho, 1995, De-noising by soft-thresholding, IEEE Trans. Inform. Theory, 41, 613, 10.1109/18.382009
Xiu, 2019, Alternating direction method of multipliers for nonconvex fused regression problems, Comput. Statist. Data Anal., 136, 59, 10.1016/j.csda.2019.01.002
Downs, 1993, A plant-wide industrial process control problem, Comput. Chem. Eng., 17, 245, 10.1016/0098-1354(93)80018-I