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A New Kernel-Based Classification Algorithm for Systems Monitoring: Comparison with Statistical Process Control Methods

  • Research Article - Systems Engineering
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Abstract

The paper presents a new Kernel-based monitoring algorithm compared with statistical process control methods, such as DISSIM and MS-PCA and some others methods widely used in process control applications. The proposed algorithm is a modified version of the well known support vector domain description (SVDD). The last one is commonly used for one-classification problems, named also novelty detection. In this paper, we have used a modified SVDD endowed with useful tools to manage multi-classification problems. The proposed classifier is also able to deal with stationary as well as non-stationary data. The principle is based on the dynamic update of the training set through a recursive deletion/insertion procedure according to adequate rules. In order to reduce the computational complexity and improve the rapidity of convergence, the algorithm considers in each run a limited frame of samples for the training process. To prove its effectiveness, the approach is assessed at first on artificially generated data. Then, we have performed a case study applied on chemical process.

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Authors and Affiliations

  1. Conception and Control of Systems Laboratory (LR-11-ES20), National Engineering School of Tunis Analysis, University of Tunis El Manar, BP, 37, Le BELVEDERE, 1002, Tunis, Tunisia

    Foued Theljani, Kaouther Laabidi, Salah Zidi & Moufida Ksouri

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  1. Foued Theljani
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  2. Kaouther Laabidi
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  3. Salah Zidi
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  4. Moufida Ksouri
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Correspondence to Foued Theljani.

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Theljani, F., Laabidi, K., Zidi, S. et al. A New Kernel-Based Classification Algorithm for Systems Monitoring: Comparison with Statistical Process Control Methods. Arab J Sci Eng 40, 645–658 (2015). https://doi.org/10.1007/s13369-014-1519-3

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  • DOI: https://doi.org/10.1007/s13369-014-1519-3

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