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Comparison of Data Depth Calculation Method for Fault Detection in Electric Signal

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Advanced, Contemporary Control (PCC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 709))

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Abstract

Functional Data Analysis (FDA) is a modern statistical technique that deals with data in the form of curves or functions. It has recently gained popularity in the field of fault detection as it can capture the dynamic behavior of a system over time. In this paper, we explore the fault detection capabilities of functional data depth calculated by different methods.

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Acknowledgements

Work partially realised in the scope of project titled ”Process Fault Prediction and Detection”. Project was financed by The National Centre for Research and Development on the base of decision no. UMO-2021/41/B/ST7/03851. Part of work was funded by AGH’s Research University Excellence Initiative under project “Interpretable methods of process diagnosis using statistics and machine learning”.

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Authors and Affiliations

  1. Department of Automatic Control and Robotics, AGH University of Science and Technology, Krakow, Poland

    Waldemar Bauer, Adrian Dudek & Jerzy Baranowski

Authors
  1. Waldemar Bauer
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  2. Adrian Dudek
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  3. Jerzy Baranowski
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Corresponding author

Correspondence to Waldemar Bauer .

Editor information

Editors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Marek Pawelczyk

  2. Silesian University of Technology, Gliwice, Poland

    Dariusz Bismor

  3. Silesian University of Technology, Gliwice, Poland

    Szymon Ogonowski

  4. Systems Research Institute, Polish Academy of Science, Warsaw, Poland

    Janusz Kacprzyk

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Bauer, W., Dudek, A., Baranowski, J. (2023). Comparison of Data Depth Calculation Method for Fault Detection in Electric Signal. In: Pawelczyk, M., Bismor, D., Ogonowski, S., Kacprzyk, J. (eds) Advanced, Contemporary Control. PCC 2023. Lecture Notes in Networks and Systems, vol 709. Springer, Cham. https://doi.org/10.1007/978-3-031-35173-0_5

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