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Fuzzy Rule-Based Anomaly Explanation in Micro-electromechanical Systems

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Advances in Computational Collective Intelligence (ICCCI 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2166))

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Abstract

This paper illustrates the application of a fuzzy rule-based system to enhance explainability, thereby reducing the time required to identify anomalous data behavior. The proposed algorithm, integrated with an autoencoder model, is employed in the field of anomaly detection, which consists of searching for small amounts of anomalies within extensive datasets.

The data analyzed is sourced from the production of a MEMS (Micro-Electromechanical System) based inertial sensor employed in the automotive industry. Therefore, the paper addresses real-world challenges encountered in industry, with the suggested method’s results validated by domain experts.

The article places a strong emphasis on explainability through the utilization of a fuzzy rule-based system, which greatly facilitates and shortens the time of decision and intervention in the industrial environment.

The fuzzy system is trained by the bacterial memetic algorithm, which combines the bacterial evolutionary algorithm with the Levenberg-Marquardt local search technique, thus providing an efficient optimization for the model. Leveraging the white-box behavior of the fuzzy system, the trained model is then utilized to generate comprehensive linguistic interpretations, which can be readily understood by MEMS experts .

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Notes

  1. 1.

    System configuration: Intel(R) Xeon(R) Gold 6248R CPU @ 3.00GHz, 3 x NVIDIA Tesla T4 (16 GB) GPU, 256 GB RAM.

References

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  7. Hunor István Lukács, Tamás Fischl, and János Botzheim. “Bacterial Evolutionary Algorithm Based Autoencoder Architecture Search for Anomaly Detection”. In: Advances in Computational Collective Intelligence. Springer Nature Switzerland, 2023, pp. 560-572. isbn: 978-3-031-41774-0

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Author information

Authors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Informatics, ELTE Eötvös Loránd University, Pázmány P. Sétány 1/A, 1117, Budapest, Hungary

    Hunor István Lukács & János Botzheim

  2. Robert Bosch Kft, Gyömrői út 104, Budapest, 1103, Hungary

    Hunor István Lukács & Tamás Fischl

Authors
  1. Hunor István Lukács
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  2. Tamás Fischl
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  3. János Botzheim
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Corresponding author

Correspondence to Hunor István Lukács .

Editor information

Editors and Affiliations

  1. and Technology, Wrocław University of Science, Wrocław, Poland

    Ngoc-Thanh Nguyen

  2. University of Leipzig, Leipzig, Germany

    Bogdan Franczyk

  3. University of Leipzig, Leipzig, Sachsen, Germany

    André Ludwig

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Nunez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. University of Münster, Münster, Germany

    Gottfried Vossen

  7. and Technology, Wrocław University of Science, Wrocław, Poland

    Adrianna Kozierkiewicz

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Lukács, H.I., Fischl, T., Botzheim, J. (2024). Fuzzy Rule-Based Anomaly Explanation in Micro-electromechanical Systems. In: Nguyen, NT., et al. Advances in Computational Collective Intelligence. ICCCI 2024. Communications in Computer and Information Science, vol 2166. Springer, Cham. https://doi.org/10.1007/978-3-031-70259-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-70259-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-70258-7

  • Online ISBN: 978-3-031-70259-4

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