On the use of reconstruction-based contribution for fault diagnosis
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Donghua Zhou | Xiao He | Hongquan Ji | Xiao He | Donghua Zhou | Hongquan Ji
[1] Barry M. Wise,et al. The process chemometrics approach to process monitoring and fault detection , 1995 .
[2] S. Qin,et al. Determining the number of principal components for best reconstruction , 2000 .
[3] Thomas F. Edgar,et al. Identification of faulty sensors using principal component analysis , 1996 .
[4] Ali Cinar,et al. Statistical process monitoring and disturbance diagnosis in multivariable continuous processes , 1996 .
[5] S. Joe Qin,et al. Statistical process monitoring: basics and beyond , 2003 .
[6] 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 .
[7] Richard D. Braatz,et al. Fault Detection and Diagnosis in Industrial Systems , 2001 .
[8] Derong Liu,et al. A Data-Based State Feedback Control Method for a Class of Nonlinear Systems , 2013, IEEE Transactions on Industrial Informatics.
[9] Gilles Mourot,et al. An improved PCA scheme for sensor FDI: Application to an air quality monitoring network , 2006 .
[10] Hao Ye,et al. Weighted Reconstruction-Based Contribution for Improved Fault Diagnosis , 2013 .
[11] Furong Gao,et al. Review of Recent Research on Data-Based Process Monitoring , 2013 .
[12] Jef Vanlaer,et al. Analysis of smearing-out in contribution plot based fault isolation for Statistical Process Control , 2013 .
[13] Frank L. Lewis,et al. Guest Editorial Data-Based Control, Modeling, and Optimization , 2011, IEEE Transactions on Neural Networks.
[14] Baligh Mnassri,et al. Reconstruction-based contribution approaches for improved fault diagnosis using principal component analysis , 2015 .
[15] Tao Chen,et al. Reconstruction-based multivariate contribution analysis for fault isolation: A branch and bound approach , 2012 .
[16] Steven X. Ding,et al. Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results , 2014 .
[17] 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.
[18] Carlos F. Alcala,et al. Reconstruction-based contribution for process monitoring with kernel principal component analysis , 2010, Proceedings of the 2010 American Control Conference.
[19] Tianyou Chai,et al. Multi-directional reconstruction based contributions for root-cause diagnosis of dynamic processes , 2014, 2014 American Control Conference.
[20] S. Joe Qin,et al. A unified geometric approach to process and sensor fault identification and reconstruction : The unidimensional fault case , 1998 .
[21] S. Qin,et al. Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods† , 1999 .
[22] Tao Chen,et al. A branch and bound method for isolation of faulty variables through missing variable analysis , 2010 .
[23] Ping Zhang,et al. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .
[24] Si-Zhao Joe Qin,et al. Reconstruction-based contribution for process monitoring , 2009, Autom..
[25] Youxian Sun,et al. Incipient Sensor Fault Diagnosis Based on Average Residual-Difference Reconstruction Contribution Plot , 2014 .
[26] S. Joe Qin,et al. Analysis and generalization of fault diagnosis methods for process monitoring , 2011 .
[27] M. J. Fuente,et al. Fault detection and isolation in transient states using principal component analysis , 2012 .
[28] Age K. Smilde,et al. Generalized contribution plots in multivariate statistical process monitoring , 2000 .
[29] Si-Zhao Joe Qin,et al. Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..
[30] Tianyou Chai,et al. Fault diagnosis of continuous annealing processes using a reconstruction-based method , 2012 .
[31] S. Joe Qin,et al. Reconstruction-Based Fault Identification Using a Combined Index , 2001 .
[32] John F. MacGregor,et al. Multivariate SPC charts for monitoring batch processes , 1995 .
[33] Donghua Zhou,et al. Leakage Fault Diagnosis for an Internet-Based Three-Tank System: An Experimental Study , 2012, IEEE Transactions on Control Systems Technology.