Skip to main content
SpringerLink
Log in

Research of the New Principal Component Analysis Algorithm Based on the Dynamic Model

  • Conference paper
Book cover Proceedings of the 2011 International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE2011) November 19-20, 2011, Melbourne, Australia

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 110))

  • 2029 Accesses

Abstract

An extension algorithm, exponentially weighted dynamic principal component analysis (EWDPCA) algorithm is proposed to monitor the dynamic process, in particular for disturbances and set points changes in process. The technique is validated through simulation in the process of actual chemical polymerization. The results show that the EWDPCA algorithm can eliminate the linear relationship among variables and reflect the real-time information in dynamic process.

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

Access this chapter

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 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Wold, S.: Exponentially Weighted Moving Principal Component Analysis and Projection to Latent Structures. Chemometrics and Intelligent Laboratory Systems 23(11), 149–161 (1994)

    Article  Google Scholar 

  2. Dayal, B.S., MacGregor, J.F.: Recursive Exponentially Weighted PLS and Its Application to Adaptive Control and Prediction. Journal of Process Control 7(3), 169–179 (1997)

    Article  Google Scholar 

  3. Qin, S.J.: Recursive PLS Algorithms for Adaptive Data Monitoring. Computer and Chemical Engineering 22(4-5), 503–514 (1998)

    Article  Google Scholar 

  4. Li, W., Yue, H.H., Valle-Cervantes, S., et al.: Recursive PCA for Adaptive Process Monitoring. Journal of Process Control 10(5), 471–486 (2000)

    Article  Google Scholar 

  5. Lane, S., Martin, E.B., Morris, A.J., Gower, P.: Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process. Transactions of the Institute of Measurement and Control 25(1), 17–35 (2003)

    Article  Google Scholar 

  6. Fortescue, T.R., Kershenbaum, L.S., Ydstie, B.E.: Implementation of Self-tuning Regulators with Variable Forgetting Factors. Automatica 17, 831–835 (1981)

    Article  Google Scholar 

  7. Ku, W., Storer, R.H., Georgakis, C.: Disturbance rejection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems 30, 179–196 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Automation, Beijing Institute of Technology, Beijing, China

    You Zhou & Qinglin Wang

  2. National Laboratory of Electro-Optics System Technology, Sanhe, China

    Guangda Yang

  3. School of Automation/School of Information Engineering, Beijing Institute of Technology/Capital Normal University, Beijing, China

    Dehui Qiu

Authors
  1. You Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qinglin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guangda Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dehui Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. South China Normal University, Yifeng Dasha Building C 1707, Huang Pu Da Dao Zhong, Cui Yuan Jie 8, Tian He Qu, 510000, Guangzhou, China

    Liangzhong Jiang

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhou, Y., Wang, Q., Yang, G., Qiu, D. (2011). Research of the New Principal Component Analysis Algorithm Based on the Dynamic Model. In: Jiang, L. (eds) Proceedings of the 2011 International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE2011) November 19-20, 2011, Melbourne, Australia. Advances in Intelligent and Soft Computing, vol 110. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25185-6_64

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25185-6_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25184-9

  • Online ISBN: 978-3-642-25185-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation