Fault detection and isolation in hybrid process systems using a combined data‐driven and observer‐design methodology
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Ahmet Palazoglu | Xuefeng Yan | Chudong Tong | Nael H. El-Farra | N. El‐Farra | A. Palazoglu | Chudong Tong | Xue-feng Yan
[1] E. Lima,et al. Modeling and performance monitoring of multivariate multimodal processes , 2013 .
[2] Kup-Sze Choi,et al. Minimum-maximum local structure information for feature selection , 2013, Pattern Recognit. Lett..
[3] Si-Zhao Joe Qin,et al. Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..
[4] Uwe Kruger,et al. Statistical monitoring of complex multivariate processes : with applications in industrial process control , 2012 .
[5] Udo Schubert,et al. Input reconstruction for statistical‐based fault detection and isolation , 2012 .
[6] Ahmet Palazoglu,et al. Process pattern construction and multi-mode monitoring , 2012 .
[7] Ahmet Palazoglu,et al. Transition Process Modeling and Monitoring Based on Dynamic Ensemble Clustering and Multiclass Support Vector Data Description , 2011 .
[8] Udo Schubert,et al. Unified model-based fault diagnosis for three industrial application studies , 2011 .
[9] Nael H. El-Farra,et al. Robust fault detection and monitoring of hybrid process systems with uncertain mode transitions , 2010, 49th IEEE Conference on Decision and Control (CDC).
[10] Ping Zhang,et al. Subspace method aided data-driven design of fault detection and isolation systems , 2009 .
[11] Nael H. El-Farra,et al. Robust actuator fault isolation and management in constrained uncertain parabolic PDE systems , 2009, Autom..
[12] Panagiotis D. Christofides,et al. Data-based fault detection and isolation using feedback control: Output feedback and optimality , 2009 .
[13] S. Qin,et al. Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models , 2008 .
[14] N.H. El-Farra,et al. A unified framework for detection, isolation and compensation of actuator faults in uncertain particulate processes , 2008, 2008 American Control Conference.
[15] Nina F. Thornhill,et al. A continuous stirred tank heater simulation model with applications , 2008 .
[16] Panagiotis D. Christofides,et al. Enhancing Data-based Fault Isolation Through Nonlinear Control , 2008 .
[17] Nael H. El-Farra,et al. Actuator fault isolation and reconfiguration in transport‐reaction processes , 2007 .
[18] S. Ding,et al. Sensor fault reconstruction and sensor compensation for a class of nonlinear state-space systems via a descriptor system approach , 2007 .
[19] Luis O. Jimenez-Rodriguez,et al. Unsupervised Linear Feature-Extraction Methods and Their Effects in the Classification of High-Dimensional Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.
[20] Si-Zhao Joe Qin,et al. An overview of subspace identification , 2006, Comput. Chem. Eng..
[21] M. Saif,et al. Fault detection and isolation based on novel unknown input observer design , 2006, 2006 American Control Conference.
[22] Lei Xie,et al. Statistical Monitoring of Dynamic Multivariate Processes - Part 1. Modeling Autocorrelation and Cross-correlation , 2006 .
[23] Jiawei Han,et al. Document clustering using locality preserving indexing , 2005, IEEE Transactions on Knowledge and Data Engineering.
[24] Darci Odloak,et al. Observer-based fault diagnosis in chemical plants , 2005 .
[25] Salvador García Muñoz,et al. Data-based latent variable methods for process analysis, monitoring and control , 2005, Comput. Chem. Eng..
[26] S. Zhao,et al. Monitoring of Processes with Multiple Operating Modes through Multiple Principle Component Analysis Models , 2004 .
[27] S. Joe Qin,et al. Statistical process monitoring: basics and beyond , 2003 .
[28] Yi Xiong,et al. Unknown disturbance inputs estimation based on a state functional observer design , 2003, Autom..
[29] Raghunathan Rengaswamy,et al. A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..
[30] Theodora Kourti,et al. Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start‐ups and grade transitions , 2003 .
[31] Anil K. Jain,et al. Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[32] Seongkyu Yoon,et al. Statistical and causal model‐based approaches to fault detection and isolation , 2000 .
[33] B. Moor,et al. Subspace state space system identification for industrial processes , 1998 .
[34] Jie Chen,et al. Observer-based fault detection and isolation: robustness and applications , 1997 .
[35] Shao-Kung Chang,et al. Design of general structured observers for linear systems with unknown inputs , 1997 .
[36] N. Lawrence Ricker,et al. Decentralized control of the Tennessee Eastman Challenge Process , 1996 .
[37] B. Moor,et al. Subspace identification for linear systems , 1996 .
[38] Mehrdad Saif,et al. A new approach to robust fault detection and identification , 1993 .
[39] E. F. Vogel,et al. A plant-wide industrial process control problem , 1993 .