Adaptive PCA based fault diagnosis scheme in imperial smelting process

In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio(GLR) test and Singular Value Decomposition(SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to eliminate false alarms and isolate faults efficiently.

[1]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[2]  S. Ding,et al.  Recursive Identification Algorithm for Parity Space Based Fault Detection Systems , 2009 .

[3]  Steven X. Ding,et al.  Recursive identification algorithms to design fault detection systems , 2010 .

[4]  Furong Gao,et al.  Review of Recent Research on Data-Based Process Monitoring , 2013 .

[5]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[6]  Ping Zhang,et al.  On the application of PCA technique to fault diagnosis , 2010 .

[7]  Si-Zhao Joe Qin,et al.  Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..

[8]  Weihua Gui,et al.  On-line forecasting model for zinc output based on self-tuning support vector regression and its application , 2004 .

[9]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[10]  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).

[11]  Weihua Li,et al.  Isolation enhanced principal component analysis , 1999 .

[12]  桂卫华,et al.  On-line forecasting model for zinc output based on self-tuning support vector regression and its application , 2004 .

[13]  Tao Chen,et al.  Robust probabilistic PCA with missing data and contribution analysis for outlier detection , 2009, Comput. Stat. Data Anal..

[14]  Kaixiang Peng,et al.  A Novel Scheme for Key Performance Indicator Prediction and Diagnosis With Application to an Industrial Hot Strip Mill , 2013, IEEE Transactions on Industrial Informatics.

[15]  Weihua Li,et al.  Recursive PCA for adaptive process monitoring , 1999 .

[16]  Steven X. Ding,et al.  Adaptive Process Monitoring Based on Parity Space Methods , 2009 .

[17]  Tsuhan Chen,et al.  Eigenspace updating for non-stationary process and its application to face recognition , 2003, Pattern Recognit..

[18]  A.A. Safavi,et al.  Enhanced Neural Network Based Fault Detection of a VVER Nuclear Power Plant With the Aid of Principal Component Analysis , 2008, IEEE Transactions on Nuclear Science.

[19]  Alkan Alkaya,et al.  Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application. , 2011, ISA transactions.

[20]  Benoît Champagne,et al.  Adaptive eigendecomposition of data covariance matrices based on first-order perturbations , 1994, IEEE Trans. Signal Process..

[21]  Si-Zhao Joe Qin,et al.  Reconstruction-based contribution for process monitoring , 2009, Autom..

[22]  U. Kruger,et al.  Moving window kernel PCA for adaptive monitoring of nonlinear processes , 2009 .

[23]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[24]  Lamiaa M. Elshenawy,et al.  Efficient Recursive Principal Component Analysis Algorithms for Process Monitoring , 2010 .

[25]  Muhammad Riaz,et al.  An improved PCA method with application to boiler leak detection. , 2005, ISA transactions.

[26]  S. Qin.  Recursive PLS algorithms for adaptive data modeling , 1998 .