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A Comparative Analysis of Anomaly Detection Methods for Predictive Maintenance in SME

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Database and Expert Systems Applications - DEXA 2022 Workshops (DEXA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1633))

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Abstract

Predictive maintenance is a crucial strategy in smart industries and plays an important role in small and medium-sized enterprises (SMEs) to reduce the unexpected breakdown. Machine failures are due to unexpected events or anomalies in the system. Different anomaly detection methods are available in the literature for the shop floor. However, the current research lacks SME-specific results with respect to comparison between and investment in different available predictive maintenance (PdM) techniques. This applies specifically to the task of anomaly detection, which is the crucial first step in the PdM workflow. In this paper, we compared and analyzed multiple anomaly detection methods for predictive maintenance in the SME domain. The main focus of the current study is to provide an overview of different unsupervised anomaly detection algorithms which will enable researchers and developers to select appropriate algorithms for SME solutions. Different Anomaly detection algorithms are applied to a data set to compare the performance of each algorithm. Currently, the study is limited to unsupervised algorithms due to limited resources and data availability. Multiple metrics are applied to evaluate these algorithms. The experimental results show that Local Outlier Factor and One-Class SVM performed better than the rest of the algorithms.

Supported by SCCH , Austria.

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Acknowledgement

The research reported in this paper has been funded by the European Interreg Austria-Czech Republic project “PredMAIn (ATCZ279)".

Author information

Authors and Affiliations

  1. Sino-Pak Center for Artificial Intelligence, PAF-IAST, Haripur, Pakistan

    Muhammad Qasim, Maqbool Khan & Waqar Mehmood

  2. Software Competence Center Hagenberg (SCCH), Hagenberg, Austria

    Maqbool Khan, Florian Sobieczky, Mario Pichler & Bernhard Moser

  3. Johannes Kepler University (JKU), Linz, Austria

    Waqar Mehmood

Authors
  1. Muhammad Qasim
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  2. Maqbool Khan
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  3. Waqar Mehmood
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  4. Florian Sobieczky
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  5. Mario Pichler
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  6. Bernhard Moser
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Corresponding authors

Correspondence to Muhammad Qasim or Maqbool Khan .

Editor information

Editors and Affiliations

  1. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Gabriele Kotsis

  2. Technical University of Vienna, Vienna, Austria

    A Min Tjoa

  3. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

  4. Software Competence Center Hagenberg, Hagenberg, Austria

    Bernhard Moser

  5. (WU) Vienna University of Economics and Business, Vienna, Austria

    Alfred Taudes

  6. Johannes Kepler University of Linz, Linz, Austria

    Atif Mashkoor

  7. Johannes Kepler University of Linz, Linz, Austria

    Johannes Sametinger

  8. Software Competence Center Hagenberg, Hagenberg, Austria

    Jorge Martinez-Gil

  9. Software Competence Center Hagenberg, Hagenberg, Austria

    Florian Sobieczky

  10. Software Competence Center Hagenberg, Hagenberg, Austria

    Lukas Fischer

  11. Software Competence Center Hagenberg, Hagenberg, Austria

    Rudolf Ramler

  12. Pak-Austria Fachhochschule - Institute of Applied Sciences and Technology (PAF-IAST), Haripur, Pakistan

    Maqbool Khan

  13. Software Competence Center Hagenberg, Hagenberg, Austria

    Gerald Czech

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Qasim, M., Khan, M., Mehmood, W., Sobieczky, F., Pichler, M., Moser, B. (2022). A Comparative Analysis of Anomaly Detection Methods for Predictive Maintenance in SME. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-14343-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14342-7

  • Online ISBN: 978-3-031-14343-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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