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On-Line Nonlinear Process Monitoring Using Kernel Principal Component Analysis and Neural Network

  • Zhong-Gai Zhao
  • Fei Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

As a valid statistical tool, principal component analysis (PCA) has been widely used in industrial process monitoring. But due to its intrinsic linear character, it performs badly in nonlinear process monitoring. Kernel PCA (KPCA) can extract useful information in nonlinear data. However KPCA-based monitoring is not suitable for on-line monitoring because of large calculation and much memory occupation. The paper introduces an on-line monitoring method based on KPCA and neural network (NN), where KPCA is used to extract nonlinear principal components (PCs) and then NN approximates the relationship between process data and nonlinear PCs. We can obtain nonlinear PCs by NN to compute the monitoring indices and then achieve the on-line monitoring. The case study shows the validity of the method.

Keywords

Neural Network Kernel Principal Component Analysis Monitoring Index Memory Occupation Complex Industrial Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Venkatasubramanian, V., et al.: A Review of Process Fault Detection and Diagnosis. Computers and Chemical Engineering 27(3), 327–346 (2003)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Schöpf, B., et al.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10(5), 1000–1016 (1998)Google Scholar
  3. 3.
    Lee, J.M., et al.: Nonlinear Process Monitoring Using Kernel Principal Component Analysis. Chemical Engineering Science 59(1), 223–234 (2004)CrossRefGoogle Scholar
  4. 4.
    Cho, J.H., et al.: Fault Identification for Process Monitoring Using Kernel Principal Component Analysis. Chemical Engineering Science 60(1), 279–288 (2005)CrossRefGoogle Scholar
  5. 5.
    Choi, S.W., et al.: Fault Detection and Identification of Nonlinear Processes Based on Kernel PCA. Chemometrics and Intelligent Laboratory Systems 75(1), 55–67 (2005)CrossRefGoogle Scholar
  6. 6.
    Jade, A.M.: Feature Extraction and Denoising Using Kernel PCA. Chemical Engineering Science 58(19), 4441–4448 (2003)CrossRefGoogle Scholar
  7. 7.
    Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, London (2001)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhong-Gai Zhao
    • 1
  • Fei Liu
    • 1
  1. 1.Institute of AutomationSouthern Yangtze UniversityWuxiP.R. China

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