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Anomaly Detection for Hydraulic Power Units—A Case Study

Author

Listed:
  • Paweł Fic

    (Department of Automatic Control and Robotics, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Adam Czornik

    (Department of Automatic Control and Robotics, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Piotr Rosikowski

    (PONAR Wadowice S.A., Św. Jana Pawła II 10, 43-170 Łaziska Górne, Poland)

Abstract

This article aims to present the real-world implementation of an anomaly detection system of a hydraulic power unit. Implementation involved the Internet of Things approach. A detailed description of the system architecture is provided. The complete path from sensors through PLC and the edge computer to the cloud is presented. Some technical information about hydraulic power units is also given. This article involves the description of several model-at-scale deployment techniques. In addition, the approach to the synthesis of anomaly and novelty detection models was described. Anomaly detection of data acquired from the hydraulic power unit was carried out using two approaches, statistical and black-box, involving the One Class SVM model. The costs of cloud resources and services that were generated in the project are presented. Since the article describes a commercial implementation, the results have been presented as far as the formal and business conditions allow.

Suggested Citation

  • Paweł Fic & Adam Czornik & Piotr Rosikowski, 2023. "Anomaly Detection for Hydraulic Power Units—A Case Study," Future Internet, MDPI, vol. 15(6), pages 1-29, June.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:6:p:206-:d:1162821
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    References listed on IDEAS

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    1. Baifan Zhou & Tim Pychynski & Markus Reischl & Evgeny Kharlamov & Ralf Mikut, 2022. "Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1139-1163, April.
    2. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    3. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
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