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Temperature Sensor Faults Monitoring in a Heat Exchanger Using Evolving Fuzzy Classification

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Advances in Computing Systems and Applications (CSA 2018)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 50))

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Abstract

In this paper, an advanced evolving clustering strategy is used to design a fuzzy model-based sensor faults detection mechanism for a pilot parallel-type heat exchanger. The change in the process operating mode is detected by an incremental unsupervised clustering procedure based on participatory learning. Real experimental data is used to construct signals for fuzzy residual generation. The resulting residuals are then processed by the evolving classifier to supervise the heat exchanger operation. The obtained results clearly show the ability of the evolving fuzzy classifier to early detect the considered temperature sensor faults.

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Author information

Authors and Affiliations

  1. Applied Automation Laboratory, M’hamed Bougara University of Boumerdès, Av. de l’indépendance, Boumerdès, Algeria

    Meryem Mouhoun & Hacene Habbi

Authors
  1. Meryem Mouhoun
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  2. Hacene Habbi
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Corresponding author

Correspondence to Meryem Mouhoun .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Oualid Demigha

  2. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Badis Djamaa

  3. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Abdenour Amamra

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Mouhoun, M., Habbi, H. (2019). Temperature Sensor Faults Monitoring in a Heat Exchanger Using Evolving Fuzzy Classification. In: Demigha, O., Djamaa, B., Amamra, A. (eds) Advances in Computing Systems and Applications. CSA 2018. Lecture Notes in Networks and Systems, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-98352-3_33

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