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Current Trends in Reliability, Availability, Maintainability and Safety pp 113–124Cite as

Nonlinear Process Monitoring Using Genetic Algorithms

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Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

This paper suggests a new approach for fault detection using Genetic Algorithms (GAs). GAs are used to find the principal curve that summarize the data. The principal curve is a generation of linear Principal Component Analysis (PCA). Introduced by Hastie as a parametric curve, the original definition is based on the self-consistency property. The Hastie’s theory encloses weaknesses in case of complex data structures or data with intersections. The existing principal curves methods employ the first component of the data as an initial estimation of principal curve that passes satisfactorily through the middle of data. However the needing of an initial line is the major inconvenient of this approach. In this work, we extend this problem in two ways. First, we introduce a new method based on GAs to find the principal curve. Second, potential application of principal curves in fault detection is proposed. An example is presented to prove the efficiency of the proposed algorithm to fault detection of nonlinear process.

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Author information

Authors and Affiliations

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

    Tawfik Najeh, Achraf Jaber Telmoudi & Lotfi Nabli

Authors
  1. Tawfik Najeh
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  2. Achraf Jaber Telmoudi
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  3. Lotfi Nabli
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Corresponding author

Correspondence to Tawfik Najeh .

Editor information

Editors and Affiliations

  1. Operation and Maintenance Engineering, Luleå, Sweden

    Uday Kumar

  2. Operation and Maintenance Engineering, Luleå, Sweden

    Alireza Ahmadi

  3. ATOM, University College, Haugesund, Norway

    Ajit Kumar Verma

  4. Bhabha Atomic Research Centre, Research Reactor Services Division, Mumbai, India

    Prabhakar Varde

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© 2016 Springer International Publishing Switzerland

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Najeh, T., Telmoudi, A.J., Nabli, L. (2016). Nonlinear Process Monitoring Using Genetic Algorithms. In: Kumar, U., Ahmadi, A., Verma, A., Varde, P. (eds) Current Trends in Reliability, Availability, Maintainability and Safety. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-23597-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-23597-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23596-7

  • Online ISBN: 978-3-319-23597-4

  • eBook Packages: EngineeringEngineering (R0)

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