Skip to main content
SpringerLink
Log in

A new fault detection method for nonlinear process monitoring

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Kernel Principal Component Analysis (KPCA) is a nonlinear extension of Principal Component Analysis (PCA). Recently, it is the most popular technique for monitoring nonlinear processes. However, the time-varying property of the industrial processes requires the adaptive ability of the KPCA. Therefore, in this paper, a Variable Moving Window Kernel PCA (VMWKPCA) method is proposed to update the KPCA model. The concept of this method consists of varying the size of the moving window according to the change of the normal process. To evaluate the performance of the proposed method, the VMWKPCA is applied for monitoring a Continuous Stirred Tank Reactor (CSTR) and a Tennessee Eastman process (TE). The results are satisfactory compared to the conventional Moving Window Kernel PCA (MWKPCA) and the Adaptive Kernel PCA (AKPCA).

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chiang LH, Russell FL, Braatz RD (2001) Fault detection and diagnosis in industrial systems. Springer, London

    Book  MATH  Google Scholar 

  2. Dunia R, Qin SJ, Edgar TF, McAvoy TJ (1996) Identification of faulty sensors using principal component analysis. AIChE J 42(10):2797–2811

    Article  Google Scholar 

  3. Scholkopf B, Smola A, Muller K (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

    Article  Google Scholar 

  4. Lee JM, Yoo C, Choi SK, Vanrolleghemb PA, Lee I (2004) Nonlinear process monitoring using kernel principal component analysis. Chem Eng Sci 59:223–234

    Article  Google Scholar 

  5. Choi SW, Lee C, Lee JMP, Park JH, Lee IB (2005) Fault detection and Identification of nonlinear processes based on kernel PCA. Chemom Intell Lab Syst 75:55–67

    Article  Google Scholar 

  6. Xie L, Wang SQ (2007) Recursive kernel PCA and its application in adaptive monitoring of nonlinear processes. J Chem Industry Eng 58(7):1776–1782

    Google Scholar 

  7. Xueqin L, Uwe TL, Lei X, Shuqing W (2009) Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemom Intell Lab Syst 96:132–143

    Article  Google Scholar 

  8. He XB, Yang YP (2008) Variable MWPCA for adaptive process monitoring. Ind Eng Chem Res 47:419–427

    Article  Google Scholar 

  9. Shrager RI, Hendler RW (1982) Titration of individual components in a mixture with resolution of difference spectra, pKs, and redox transition. Anal Chem 54(7):1147–1152

    Article  Google Scholar 

  10. Wang X, Kruger U, Irwin GW (2005) Process monitoring approach using fast moving window PCA. Ind Eng Chem Res 44:5691–5702

    Article  Google Scholar 

  11. Jeng JC (2010) Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms. J Taiwan Inst Chem Eng 41:475–481

    Article  Google Scholar 

  12. Yang Q, Tian F, Wang D (2010) Online approach of fault diagnosis based on lifting wavelets and moving window PCA. Proceedings of the 8th World Congress on Intelligent Control and Automation, Jinan, China

  13. Chouaib C, Mohamed FH, Messaoud D (2015) New adaptive kernel principal component analysis for nonlinear dynamic process monitoring. Appl Math Inf Sci 9(4):1833–1845

    Google Scholar 

  14. Yunpeng F, Wei Z, Yingwei (2014) Monitoring of nonlinear time-delay processes based on adaptive method and moving window. Hindawi Publ Corp Math Problems Eng 10:1155–546138

    Google Scholar 

  15. Tracy ND, Young JC, Mason RL (1992) Multivariate control charts for individual observations. J Qual Control 24(2):88–95

    Google Scholar 

  16. Tates AA, Louwerse DJ, Smilde AK, Koot GLM, Berndt H (1999) Monitoring a PVC batch process with multivariate statistical process control charts. Ind Eng Chem Res 38:4769–4776

    Article  Google Scholar 

  17. Nomikos P, MacGregor JF (1995) Multivariate SPC charts for monitoring batch processes. Technometrics 37:41–59

    Article  MATH  Google Scholar 

  18. Okba T, Ilyes E, Messaoud H (2012) Online identification of nonlinear system using reduced kernel principal component analysis. Neural Comput 21:161–169

    Article  Google Scholar 

  19. Ilyes E, Ines J, Okba T, Hassani M (2013) Online prediction model based on the SVD–KPCA method. ISA Trans 52:96–104

    Article  Google Scholar 

  20. Downs JJ, Vogel EF (1993) A plant-wide industrial process control problem. Comput Chem Eng 17:245–255

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, Monastir, 5019, Tunisia

    Radhia Fazai, Okba Taouali & Nasereddine Bouguila

  2. Badji Mokthar-Anaba University, BP.12, Anaba, 2300, Algeria

    Mohamed Faouzi Harkat

Authors
  1. Radhia Fazai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Okba Taouali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Faouzi Harkat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nasereddine Bouguila
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Okba Taouali.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fazai, R., Taouali, O., Harkat, M.F. et al. A new fault detection method for nonlinear process monitoring. Int J Adv Manuf Technol 87, 3425–3436 (2016). https://doi.org/10.1007/s00170-016-8745-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-8745-7

Keywords

Search

Navigation