Survey on data-driven industrial process monitoring and diagnosis
暂无分享,去创建一个
[1] Thomas F. Edgar,et al. Identification of faulty sensors using principal component analysis , 1996 .
[2] S. de Jong,et al. A framework for sequential multiblock component methods , 2003 .
[3] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[4] Zhi-huan Song,et al. Process Monitoring Based on Independent Component Analysis - Principal Component Analysis ( ICA - PCA ) and Similarity Factors , 2007 .
[5] Ali Cinar,et al. Statistical process monitoring and disturbance diagnosis in multivariable continuous processes , 1996 .
[6] Leo H. Chiang,et al. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis , 2000 .
[7] Barry M. Wise,et al. RECENT ADVANCES IN MULTIVARIATE STATISTICAL PROCESS CONTROL: IMPROVING ROBUSTNESS AND SENSITIVITY , 1991 .
[8] Wallace E. Larimore,et al. Statistical optimality and canonical variate analysis system identification , 1996, Signal Process..
[9] Donghua Zhou,et al. Geometric properties of partial least squares for process monitoring , 2010, Autom..
[10] Yale Zhang,et al. Online monitoring of steel casting processes using multivariate statistical technologies: From continuous to transitional operations☆ , 2006 .
[11] A. J. Morris,et al. Non-parametric confidence bounds for process performance monitoring charts☆ , 1996 .
[12] Q. Peter He,et al. A New Fault Diagnosis Method Using Fault Directions in Fisher Discriminant Analysis , 2005 .
[13] Carlos F. Alcala,et al. Reconstruction-based contribution for process monitoring with kernel principal component analysis , 2010, Proceedings of the 2010 American Control Conference.
[14] Dale E. Seborg,et al. Process monitoring based on canonical variate analysis , 1997, 1997 European Control Conference (ECC).
[15] T. Harris. Assessment of Control Loop Performance , 1989 .
[16] Manabu Kano,et al. Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry , 2008, Comput. Chem. Eng..
[17] A. J. Willis. Condition monitoring of centrifuge vibrations using kernel PLS , 2010, Comput. Chem. Eng..
[18] John Moody,et al. Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.
[19] Donghua Zhou,et al. Total projection to latent structures for process monitoring , 2009 .
[20] Manabu Kano,et al. Comparison of statistical process monitoring methods: application to the Eastman challenge problem , 2000 .
[21] David M. Himmelblau,et al. FAULT DIAGNOSIS IN COMPLEX CHEMICAL PLANTS USING ARTIFICIAL NEURAL NETWORKS , 1991 .
[22] Richard D. Braatz,et al. Fault Detection and Diagnosis in Industrial Systems , 2001 .
[23] Hong Zhou,et al. Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares , 2010, IEEE Transactions on Industrial Informatics.
[24] Arturo Roman-Messina,et al. Monitoring the Health of Large-Scale Power Systems: A Near Real-Time Perspective , 2012 .
[25] E. Martin,et al. Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring , 2006 .
[26] B. M. Wise,et al. UPSET AND SENSOR FAILURE DETECTION IN MULTIVARIATE PROCESSES , 1989 .
[27] S. Joe Qin,et al. Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark , 2008 .
[28] Seongkyu Yoon,et al. Statistical and causal model‐based approaches to fault detection and isolation , 2000 .
[29] Catherine Porte,et al. Automation and optimization of glycine synthesis , 1996 .
[30] S. Qin,et al. Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models , 2008 .
[31] D. Hawkins. Multivariate quality control based on regression-adjusted variables , 1991 .
[32] Age K. Smilde. Comments on three‐way analyses used for batch process data , 2001 .
[33] S. Qin. Recursive PLS algorithms for adaptive data modeling , 1998 .
[34] C. T. Seppala,et al. Recent Developments in Controller Performance Monitoring and Assessment Techniques , 2002 .
[35] Chonghun Han,et al. Fault Detection and Operation Mode Identification Based on Pattern Classification with Variable Selection , 2004 .
[36] J. Edward Jackson,et al. A User's Guide to Principal Components. , 1991 .
[37] Jon Rigelsford,et al. Fault Detection and Diagnosis in Industrial Systems Advanced Textbooks in Control and Signal Processing , 2001 .
[38] Barry M. Wise,et al. The process chemometrics approach to process monitoring and fault detection , 1995 .
[39] Asoke K. Nandi,et al. Support vector machines for detection and characterization of rolling element bearing faults , 2001 .
[40] Steven X. Ding,et al. Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .
[41] Jin Hyun Park,et al. Fault detection and identification of nonlinear processes based on kernel PCA , 2005 .
[42] Roman Rosipal,et al. Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space , 2002, J. Mach. Learn. Res..
[43] Weihua Li,et al. Detection, identification, and reconstruction of faulty sensors with maximized sensitivity , 1999 .
[44] H. Yue,et al. Fault detection of plasma etchers using optical emission spectra , 2000 .
[45] Theodora Kourti,et al. Multivariate SPC Methods for Process and Product Monitoring , 1996 .
[46] Jie Zhang,et al. Fault Detection of Plasma Etchers using Big Data Analysis , 2014 .
[47] In-Beum Lee,et al. Fault detection and diagnosis based on modified independent component analysis , 2006 .
[48] Seongkyu Yoon,et al. Fault diagnosis with multivariate statistical models part I: using steady state fault signatures , 2001 .
[49] Christos Georgakis,et al. Disturbance detection and isolation by dynamic principal component analysis , 1995 .
[50] S. Joe Qin,et al. A unified geometric approach to process and sensor fault identification and reconstruction : The unidimensional fault case , 1998 .
[51] S. Qin,et al. Fault Diagnosis in the Feedback-Invariant Subspace of Closed-Loop Systems , 2005 .
[52] Robert E. Uhrig,et al. Nonlinear Partial Least Squares Modeling for Instrument Surveillance and Calibration Verification , 2000 .
[53] S. Wold,et al. Multi‐way principal components‐and PLS‐analysis , 1987 .
[54] S. Joe Qin,et al. Statistical process monitoring: basics and beyond , 2003 .
[55] S. J. Qin,et al. Concurrent projection to latent structures for output-relevant and input-relevant fault monitoring , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).
[56] Rolf Isermann,et al. Fault-Diagnosis Systems , 2005 .
[57] Manabu Kano,et al. Monitoring independent components for fault detection , 2003 .
[58] George W. Irwin,et al. Fault reconstruction in linear dynamic systems using multivariate statistics , 2006 .
[59] S. Qin,et al. Output Relevant Fault Reconstruction and Fault Subspace Extraction in Total Projection to Latent Structures Models , 2010 .
[60] J. E. Jackson. A User's Guide to Principal Components , 1991 .
[61] Silvio Simani,et al. Model-Based Fault Diagnosis Techniques , 2003 .
[62] S. Wold. Exponentially weighted moving principal components analysis and projections to latent structures , 1994 .
[63] B. Bakshi. Multiscale PCA with application to multivariate statistical process monitoring , 1998 .
[64] S. Qin,et al. Detection and identification of faulty sensors in dynamic processes , 2001 .
[65] Barry M. Wise,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 , 1999 .
[66] Thomas E. Marlin,et al. Multivariate statistical monitoring of process operating performance , 1991 .
[67] Barry M. Wise,et al. Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor Etch Process: Improving Robustness through Model Updating , 1997 .
[68] C. T. Seppala,et al. A review of performance monitoring and assessment techniques for univariate and multivariate control systems , 1999 .
[69] S. Joe Qin,et al. Control performance monitoring — a review and assessment , 1998 .
[70] S. Joe Qin,et al. Data-driven Fault Detection and Diagnosis for Complex Industrial Processes , 2009 .
[71] J. E. Jackson,et al. Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .
[72] Qiang Liu,et al. Decentralized Fault Diagnosis of Continuous Annealing Processes Based on Multilevel PCA , 2013, IEEE Transactions on Automation Science and Engineering.
[73] U. Kruger,et al. Dynamic Principal Component Analysis Using Subspace Model Identification , 2005, ICIC.
[74] S. Joe Qin,et al. Reconstruction-Based Fault Identification Using a Combined Index , 2001 .
[75] Manabu Kano,et al. Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem , 2002 .
[76] S. Joe Qin,et al. Consistent dynamic PCA based on errors-in-variables subspace identification , 2001 .
[77] S.J. Qin,et al. Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis , 2006, IEEE Transactions on Semiconductor Manufacturing.
[78] Barry M. Wise,et al. A Theoretical Basis for the use of Principal Component Models for Monitoring Multivariate Processes , 1990 .
[79] Furong Gao,et al. A survey on multistage/multiphase statistical modeling methods for batch processes , 2009, Annu. Rev. Control..
[80] S. D. Jong. SIMPLS: an alternative approach to partial least squares regression , 1993 .
[81] Ali Cinar,et al. Statistical monitoring of multistage, multiphase batch processes , 2002 .
[82] Jianfei Dong,et al. Data Driven Fault Detection and Isolation of a Wind Turbine Benchmark , 2011 .
[83] Jin Cao,et al. PCA-based fault diagnosis in the presence of control and dynamics , 2004 .
[84] Donghua Zhou,et al. Generalized Reconstruction-Based Contributions for Output-Relevant Fault Diagnosis With Application to the Tennessee Eastman Process , 2011, IEEE Transactions on Control Systems Technology.
[85] Hongwei Tong,et al. Detection of gross erros in data reconciliation by principal component analysis , 1995 .
[86] In-Beum Lee,et al. Fault identification for process monitoring using kernel principal component analysis , 2005 .
[87] Mark A. Kramer,et al. Autoassociative neural networks , 1992 .
[88] Si-Zhao Joe Qin,et al. Monitoring Non-normal Data with Principal Component Analysis and Adaptive Density Estimation , 2007, 2007 46th IEEE Conference on Decision and Control.
[89] Jose A. Romagnoli,et al. Robust multi-scale principal components analysis with applications to process monitoring , 2005 .
[90] Michael S. Dudzic,et al. An industrial perspective on implementing on-line applications of multivariate statistics , 2004 .
[91] I. Jolliffe. Principal Component Analysis , 2002 .
[92] John F. MacGregor,et al. Process monitoring and diagnosis by multiblock PLS methods , 1994 .
[93] David J. Sandoz,et al. The application of principal component analysis and kernel density estimation to enhance process monitoring , 2000 .
[94] S. Wold. Nonlinear partial least squares modelling II. Spline inner relation , 1992 .
[95] Jin Hyun Park,et al. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis , 2004, Comput. Chem. Eng..
[96] Steven X. Ding,et al. Data-Driven Design of Model-Based Fault Diagnosis Systems , 2012 .
[97] S. Joe Qin,et al. Reconstruction-based Contribution for Process Monitoring , 2008 .
[98] Paul Geladi,et al. Principal Component Analysis , 1987, Comprehensive Chemometrics.
[99] Thomas J. McAvoy,et al. Nonlinear PLS Modeling Using Neural Networks , 1992 .
[100] Michael J. Piovoso,et al. On unifying multiblock analysis with application to decentralized process monitoring , 2001 .
[101] John F. MacGregor,et al. Multivariate SPC charts for monitoring batch processes , 1995 .
[102] Karlene A. Kosanovich,et al. Improved Process Understanding Using Multiway Principal Component Analysis , 1996 .
[103] David W. Scott,et al. From Kernels to Mixtures , 2001, Technometrics.
[104] A. Negiz,et al. Statistical monitoring of multivariable dynamic processes with state-space models , 1997 .
[105] S. Joe Qin,et al. Subspace approach to multidimensional fault identification and reconstruction , 1998 .
[106] Junghui Chen,et al. Mixture Principal Component Analysis Models for Process Monitoring , 1999 .
[107] S. Joe Qin,et al. Multivariate process monitoring and fault diagnosis by multi-scale PCA , 2002 .
[108] Hector Budman,et al. Fault detection, identification and diagnosis using CUSUM based PCA , 2011 .
[109] B. Kowalski,et al. Partial least-squares regression: a tutorial , 1986 .
[110] Bruce R. Kowalski,et al. Distinguishing between process upsets and sensor malfunctions using sensor redundancy , 1999 .
[111] P. Miller,et al. Contribution plots: a missing link in multivariate quality control , 1998 .
[112] Weihua Li,et al. Isolation enhanced principal component analysis , 1999 .
[113] T. McAvoy,et al. Nonlinear principal component analysis—Based on principal curves and neural networks , 1996 .
[114] Janos Gertler,et al. Fault detection and diagnosis in engineering systems , 1998 .
[115] M. Kramer. Nonlinear principal component analysis using autoassociative neural networks , 1991 .
[116] S. J. Qin,et al. Extracting fault subspaces for fault identification of a polyester film process , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).
[117] Ali Cinar,et al. Chemical Process Performance Evaluation , 2007 .
[118] Raghunathan Rengaswamy,et al. A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..
[119] Bhupinder S. Dayal,et al. Recursive exponentially weighted PLS and its applications to adaptive control and prediction , 1997 .
[120] Theodora Kourti,et al. Statistical Process Control of Multivariate Processes , 1994 .
[121] Thomas J. McAvoy,et al. Nonlinear FIR Modeling via a Neural Net PLS Approach , 1996 .
[122] J. Macgregor,et al. Monitoring batch processes using multiway principal component analysis , 1994 .
[123] S. Joe Qin,et al. Statistical MIMO controller performance monitoring. Part II: Performance diagnosis , 2008 .
[124] Tianyou Chai,et al. Fault diagnosis of continuous annealing processes using a reconstruction-based method , 2012 .
[125] Sirish L. Shah,et al. Good, bad or optimal? Performance assessment of multivariable processes , 1997, Autom..
[126] S. Joe Qin,et al. Sensor validation and process fault diagnosis for FCC units under MPC feedback , 2001 .
[127] Riccardo Leardi,et al. Industrial experiences with multivariate statistical analysis of batch process data , 2006 .
[128] S. Joe Qin,et al. Recent developments in multivariable controller performance monitoring , 2007 .
[129] Michael J. Piovoso,et al. Process data chemometrics , 1991 .
[130] S. Wold,et al. Orthogonal projections to latent structures (O‐PLS) , 2002 .
[131] Gunnar Rätsch,et al. Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.
[132] J. Macgregor,et al. Analysis of multiblock and hierarchical PCA and PLS models , 1998 .
[133] Weihua Li,et al. Recursive PCA for Adaptive Process Monitoring , 1999 .
[134] G. Box. Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification , 1954 .
[135] A. Höskuldsson. PLS regression methods , 1988 .