Skip to main content

Advertisement

SpringerLink
Log in

Genetic Algorithms Applied to PCA–Residues Optimization for Defect Localization

  • Research Article - Chemical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

While defect localization is vital in real-world systems, some limitations inherent to the existing techniques urge us to seek more advanced methods. This paper presents a new approach which takes advantages of genetic algorithms for optimization of non-convex objective function employed in calculating structured residues. The proposed approach so far improved the current principal component analysis (PCA) based on one of the defect localization. It has excellent impact on problem solving while dealing with optimization of residues structuring. The first part illustrates both the PCA model and the traditional residues structuring approach. The principle of optimizing a problem via genetic algorithms is explained later. A proposed objective function to be optimized is defined in the next part, and its optimization via genetic algorithms allows the structured residues computation. The new approach has been applied and proved functional for monitoring the Tennessee Eastman process. We have also proved the efficient performance of the proposed method in comparing it with some state-of-the-art methods.

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. Gertler, J.; Formiguera, R.: Leak detection and isolation in water distribution networks using principal components analysis and structured residuals. In: Conference on control and fault tolerant systems, pp. 91–196 (2010)

  2. Mina, J.; Verde, C.; Sanchez-Parra, M.; Ortega, F.: Fault isolation with principal components structured models for a gas turbine. In: American control conference, pp. 197–218 (2008)

  3. Tharrault Y., Mourot G., Ragot J., Maquin D.: Fault detection and isolation with robust principal component analysis. Int. J. Appl. Math. Comput. Sci. 18(4), 429–442 (2008)

    Article  MATH  Google Scholar 

  4. Napolitano M., Lacava M.: FDI structural analysis approach for the generation of structured residuals for aircraft. IEEE Trans. Aerosp. Electron. Syst. 45, 1466–1482 (2009)

    Article  Google Scholar 

  5. Li W., Qin S.J.: Optimal structured residual approach for improved faulty sensor diagnosis. Am. Chem. Soc. 44((7), 2117–2124 (2005)

    Google Scholar 

  6. Feng, H.; Li, J.H.: A new robust structured residual generation method based on residual space identification. In: 8th international conference on electronic measurement and instruments, ICEMI ’07, pp. 131–137 (2007)

  7. Gertler J., Li W.: Isolation enhanced principal component analysis. Mixed Hybrid Finite Elem. Methods 45, 323–334 (1999)

    Google Scholar 

  8. Gertler J., Cao J.: Design of optimal structured residuals from partial principal component models for fault diagnosis in linear systems. J. Process Control 15(5), 585–603 (2005)

    Article  Google Scholar 

  9. Weihua L., Sirish S.: Structured residual vector-based approach to sensor fault detection and isolation. J. Process Control 12(3), 429–443 (2002)

    Article  Google Scholar 

  10. Goldberg D., Korb B., Deb K.: Messy genetic algorithms: motivation, analysis, and first results. J. Complex Syst. 5(3), 493–530 (1989)

    MathSciNet  Google Scholar 

  11. Zhang J., Chung H., Lo W.L.: Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Trans. Evol. Comput. 11(3), 326–335 (2007)

    Article  Google Scholar 

  12. Srinivas M., Patnaik L.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst. Man Cybern. 24(4), 656–667 (1994)

    Article  Google Scholar 

  13. Akbari, R.; Ziarati, K.: A multilevel evolutionary algorithm for optimizing numerical functions. IJIEC. 2(2), 419–430 (2010)

  14. Sung-Hyuk C., Tappert C.: A genetic algorithm for constructing compact binary decision trees. J. Pattern Recognit. Res. 4, 1–13 (2009)

    Article  Google Scholar 

  15. Qin S.J., Dunia R.: Determining the number of principal components for best reconstruction. J. Process Control 10(2–3), 245–250 (2000)

    Article  Google Scholar 

  16. Schmitt L.: Theory of genetic algorithms II: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling. Theor. Comput. Sci. 3, 181–200 (2004)

    Article  Google Scholar 

  17. Sales-Cruz M.: Tennessee Eastman plant-wide industrial process. Technical report, CAPEC. Technical University of Denmark (2004)

Download references

Author information

Authors and Affiliations

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

    Tawfik Najeh, Achraf Jabeur Telmoudi & Lotfi Nabli

Authors
  1. Tawfik Najeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Achraf Jabeur Telmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lotfi Nabli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tawfik Najeh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Najeh, T., Telmoudi, A.J. & Nabli, L. Genetic Algorithms Applied to PCA–Residues Optimization for Defect Localization. Arab J Sci Eng 40, 2123–2132 (2015). https://doi.org/10.1007/s13369-015-1714-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-015-1714-x

Keywords

Search

Navigation