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Biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic

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Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13811))

Abstract

Modern high-tech industrial enterprises are equipped with sophisticated equipment and microprocessor technology. The maintenance of such production facilities is an expensive procedure, and the replacement of equipment due to breakdown or wear and tear leads to significant financial costs. In the oil and gas industry, the idle operation of an enterprise for several hours or a working day can cause serious losses to the company. In this regard, it is relevant to develop intelligent diagnostic systems aimed at timely detection of faults, assessing the degree of their criticality and predicting possible breakdowns in the future. The use of bioinspired machine learning methods for diagnosing industrial equipment in real industrial production is a promising area of research. The article presents the developed diagnostic system for industrial equipment based on the methodology of analysis of modes, failures of their influence, degree of criticality (Failure Mode and Effects Analysis, FMEA) and a unified artificial immune system (UAIS), created on the basis of systematization and classification of modified algorithms of artificial immune systems (AIS). Unification is used to select the most efficient modified AIS algorithm based on the theories of clonal selection, negative selection and the immune network for processing heterogeneous data. UAIS is especially effective in the analysis of dynamically changing production data and a small number of training samples corresponding to equipment failures. Simulation results obtained on real data of TengizChevroil refinery.

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Acknowledgements

This research has been funded by the Science Committee of the Ministry of Education and Science of the Republic Kazakhstan (Grant No. AP09258508) 2021–2023.

Author information

Authors and Affiliations

  1. Institute of Information and Computing Technologies, Almaty, Kazakhstan

    Galina Samigulina

  2. Kazakh-British Technical University, Almaty, Kazakhstan

    Zarina Samigulina

Authors
  1. Galina Samigulina
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  2. Zarina Samigulina
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Corresponding author

Correspondence to Zarina Samigulina .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Samigulina, G., Samigulina, Z. (2023). Biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_7

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  • DOI: https://doi.org/10.1007/978-3-031-25891-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25890-9

  • Online ISBN: 978-3-031-25891-6

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