Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Neural Networks

ISNN 2007: Advances in Neural Networks – ISNN 2007 pp 597–606Cite as

KPCA Plus FDA for Fault Detection

  • Conference paper

  • 1483 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4493))

Abstract

Kernel principal component analysis (KPCA) is widely used for fault detection. In this paper, a KPCA plus Fisher discriminant analysis (FDA) scheme is adopted to improve the fault detection performance of KPCA. Simulation results are given to show the effectiveness of the improvements for fault detection performance in terms of high fault detection rate.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chen, J.H., Chen, H.-H.: On-line Batch Process Monitoring using MHMT-based MPCA. Chemical Engineering Science 61, 3223–3239 (2006)

    Article  Google Scholar 

  2. Lieftucht, D., Kruger, U., Irwin, G.W.: Improved Rliability in Dagnosing Fults using Mltivariate Satistics. Computers and Chemical Engineering 30, 901–912 (2006)

    Article  Google Scholar 

  3. Detroja, K.P., Gudi, R.D., Patwardhan, S.C., Roy, K.: Fault Detection and Isolation Using Correspondence Analysis. Industrial & Engineering Chemistry Research 45, 223–235 (2006)

    Article  Google Scholar 

  4. Zuo, M.J., Lin, J., Fan, X.F.: Feature Separation using ICA for a One-dimensional Time Series and its Application in Fault Detection. Journal of Sound and Vibration 287, 614–624 (2005)

    Google Scholar 

  5. Lee, G., Han, C.H., Yoon, E.S.: Multiple-fault Diagnosis of the Tennessee Eastman Process Based on System Decomposition and Dynamic PLS. Industrial & Engineering Chemistry Research 43, 8037–8048 (2004)

    Article  Google Scholar 

  6. Kramer, M.A.: Non-linear Principal Component Analysis using Autoassociative Neural Networks. AIChE Journal 37, 233–243 (1991)

    Article  Google Scholar 

  7. Tan, S., Mavrovouniotis, M.L.: Reducing Data Dimensionality through Optimizing Neural Networks Inputs. AIChE Journal 41, 1471–1480 (1995)

    Article  Google Scholar 

  8. Jia, F., Martin, E.B., Morris, A.J.: Non-linear Principal Component Analysis for Process Fault Detection. Computers and Chemical Engineering 22, S851–S854 (1998)

    Google Scholar 

  9. Geng, Z.Q., Zhu, Q.X.: Multiscale Nonlinear Principal Component Analysis (NLPCA) and its Application for Chemical Process Monitoring. Industrial & Engineering Chemistry Research 44, 3585–3593 (2005)

    Article  Google Scholar 

  10. Chen, J.H., Liao, C.-M.: Dynamic Process Fault Monitoring Based on Neural Network and PCA. Journal of Process Control 12, 277–289 (2002)

    Article  Google Scholar 

  11. Dong, D., McAvoy, T.J.: Nonlinear Principal Component Analysis-Based on Principal Curves and Neural Networks. Computers and Chemical Engineering 20, 65–78 (1996)

    Article  Google Scholar 

  12. Schölkopf, B., Smola, A.J., Müller, K.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10, 1299–1399 (1998)

    Article  Google Scholar 

  13. Mika, S., Schölkopf, B., Smola, A.J., Müller, K.-R., Scholz, M., Rätsch, G.: KPCA and De-noising in Feature Spaces. Advances in Neural Information Processing Systems 11, 536–542 (1999)

    Google Scholar 

  14. Schölkopf, B., Mika, S., Burges, C.J.C., Knirsch, P., Müller, K.-R., Rätsch, G., Smola, A.J.: Input Space Versus Feature Space in Kernel-based Methods. IEEE Transactions on Neural Networks 10, 1000–1016 (1999)

    Article  Google Scholar 

  15. Lee, J.-M., Yoo, C.K., Choi, S.W., Vanrolleghem, P.A., Lee, I.-B.: Nonlinear Process Monitoring using Kernel Principal Component Analysis. Chemical Engineering Science 59, 223–234 (2004)

    Article  Google Scholar 

  16. Choi, S.W., Lee, C., Lee, J.-M., Park, J.H., Lee, I.-B.: Fault Detection and Identification of Nonlinear Processes Based on KPCA. Chemometrics and Intelligent Laboratory Systems 75, 55–67 (2005)

    Article  Google Scholar 

  17. Cho, J.-H., Lee, J.-M., Choi, S.W., Lee, D., Lee, I.-B.: Fault Identification for Process Monitoring using Kernel Principal Component Analysis. Chemical Engineering Science 60, 279–288 (2005)

    Article  Google Scholar 

  18. Fukunaga, K.: Introduction to Statistical Pattern Classification. Academic Press, San Diego (1990)

    Google Scholar 

  19. Wu, Y., Huang, T.S., Toyama, K.: Self-Supervised Learning for Object Based on Kernel Discriminant-EM Algorithm. In: Proceedings of International Conference on Computer Vision, Kauai, HI, vol. 1, pp. 275–280 (2001)

    Google Scholar 

  20. MacGregor, J.F., Kourti, T.: Statistical Process Control of Multivariate Processes. Control Engineering Practice 3, 403–414 (1995)

    Article  Google Scholar 

  21. Tracy, N.D., Young, J.C., Mason, R.L.: Multivariate Control Charts for Individual Observations. Journal of Quality Technology 24, 88–95 (1992)

    Google Scholar 

  22. Haykin, S.: Neural Networks. Prentice-Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  23. Chiang, L.H., Russell, F.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, London (2001)

    MATH  Google Scholar 

  24. Jackson, J.E., Mudholkar, G.S.: Control Procedures for Residuals Associated with Principal Component Analysis. Technometrics 21, 341–349 (1979)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Instrumentation Science & Opto-electronics Engineering, Beijing University of Aeronautics & Astronautics, Beijing 100083, China

    Peiling Cui & Jiancheng Fang

Authors
  1. Peiling Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiancheng Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Derong Liu Shumin Fei Zengguang Hou Huaguang Zhang Changyin Sun

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Cui, P., Fang, J. (2007). KPCA Plus FDA for Fault Detection. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72395-0_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72394-3

  • Online ISBN: 978-3-540-72395-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation