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Real-time anomaly detection system within the scope of smart factories

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Abstract

Anomaly detection is the process of identifying patterns that move differently from normal in a certain order. This process is considered one of the necessary measures for the safety of intelligent production systems. This study proposes a real-time anomaly detection system capable of using and analyzing data in smart production systems consisting of interconnected devices. Synthetic data were preferred in the study because it has difficulties such as high cost and a long time to obtain real anomaly data naturally for learning and testing processes. In order to obtain the necessary synthetic data, a simulation was developed by taking the popcorn production systems as an example. Multi-class anomalies were defined in the obtained data set, and the analysis performances were tested by creating learning models with AutoML libraries. In the field of production systems, while studies on anomaly detection generally focus on whether there is an anomaly in the system, it is aimed to determine which type of anomaly occurs in which device, together with the detection of anomaly by using multi-class tags in the data of this study. As a result of the tests, the Auto-Sklearn library presented the learning models with the highest performance on all data sets. As a result of the study, a real-time anomaly detection system was developed on dynamic data by using the obtained learning models.

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Data availability

The data used in anomaly detection, generated for analysis during the current study, are available from the corresponding author on reasonable request.

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Acknowledgements

We wish to thank the reviewers for many helpful comments and suggestions.

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Authors and Affiliations

  1. Department of Computer Technologies, Karabuk University, Karabuk, Turkey

    Cihan Bayraktar

  2. Department of Computer Engineering, Atılım University, Ankara, Turkey

    Ziya Karakaya

  3. Department of Computer Engineering, Konya Food and Agriculture University, Konya, Turkey

    Ziya Karakaya

  4. Department of Industrial Engineering, Gazi University, Ankara, Turkey

    Hadi Gökçen

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  1. Cihan Bayraktar
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All authors contributed significantly to the development of the simulation and the writing of the article. All authors read and approved the final manuscript.

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Correspondence to Cihan Bayraktar.

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Bayraktar, C., Karakaya, Z. & Gökçen, H. Real-time anomaly detection system within the scope of smart factories. J Supercomput 79, 14707–14742 (2023). https://doi.org/10.1007/s11227-023-05236-w

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