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Application of Fuzzy Logic to the Risk Assessment of Production Machines Failures

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 749))

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Abstract

Conducting any activity, regardless of its nature, involves risk. This is an aspect that is particularly relevant in the science of running processes, including manufacturing. Moreover, the intuitive and simplest way for humans to determine the risk is formulated in the form of linguistic values, which makes it difficult to analyse and evaluate by classical methods. The article briefly examines the area of application of fuzzy logic in risk assessment in research of the last two decades. The use of fuzzy set principles makes it possible to achieve benefits in the area of risk assessment, which is supported by the increasing popularity of using these methods in practice. The aim of this paper was to verify the feasibility of applying fuzzy logic principles to the risk assessment of machinery park failures in the production process. The Fuzzy Inference System (FIS) used in this case was based on the Mamdani implication. The results achieved in this case are presented. The application example discussed, in the light of the literature analysis indicated earlier, confirms not only the possibility but also the validity of applying fuzzy set principles to risk analysis in production processes.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Dagmara Łapczyńska & Anna Burduk

Authors
  1. Dagmara Łapczyńska
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  2. Anna Burduk
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Corresponding author

Correspondence to Anna Burduk .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Łapczyńska, D., Burduk, A. (2023). Application of Fuzzy Logic to the Risk Assessment of Production Machines Failures. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_4

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