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 identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently.

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

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

[3]  Changchun Hua,et al.  A SIMPLIFIED RECURSIVE DYNAMIC PCA BASED MONITORING SCHEME FOR IMPERIAL SMELTING PROCESS , 2012 .

[4]  Jiang Bin,et al.  Adaptive PCA based fault diagnosis scheme in imperial smelting process , 2013, 2013 10th IEEE International Conference on Control and Automation (ICCA).

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

[6]  P. Miller,et al.  Contribution plots: a missing link in multivariate quality control , 1998 .

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

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

[9]  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.

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

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

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

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

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

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

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

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

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

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

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

[21]  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.

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

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