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A Systematic Mapping Study on Machine Learning Techniques Applied for Condition Monitoring and Predictive Maintenance in the Manufacturing Sector

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  • Thuy Linh Jenny Phan

    (Faculty of Informatics/Mathematics, Dresden University of Applied Sciences, 01069 Dresden, Germany)

  • Ingolf Gehrhardt

    (Faculty of Informatics/Mathematics, Dresden University of Applied Sciences, 01069 Dresden, Germany)

  • David Heik

    (Faculty of Informatics/Mathematics, Dresden University of Applied Sciences, 01069 Dresden, Germany)

  • Fouad Bahrpeyma

    (Faculty of Informatics/Mathematics, Dresden University of Applied Sciences, 01069 Dresden, Germany)

  • Dirk Reichelt

    (Faculty of Informatics/Mathematics, Dresden University of Applied Sciences, 01069 Dresden, Germany)

Abstract

Background: Today’s production facilities must be efficient in both manufacturing and maintenance. Efficiency enables the company to maintain the required output while reducing production effort or costs. With the increasing interest in process automation and the Internet of things since Industry 4.0 was introduced, such shop floors are growing in complexity. Every component of the production needs to be continuously monitored, which is the basis for predictive maintenance (PdM). To predict when maintenance is needed, the components’ conditions are monitored with the help of a condition monitoring (CM) system. However, this task is difficult for human employees, as the monitoring and analysis is very demanding. To overcome this, machine learning (ML) can be applied to ensure more efficient production. Methods: This paper aims to investigate the application of ML techniques for CM and PdM in the manufacturing sector. For this reason, a systematic mapping study (SMS) is conducted in order to structure and classify the current state of research and identify potential gaps for future investigation. Relevant literature was considered between January 2011 and May 2021. Results: Based on the guidelines for SMSs and previously defined research questions, existing publications are examined and a systematic overview of the current state of the research domain is provided. Conclusions: Techniques such as reinforcement learning and transfer learning are underrepresented, but increasingly attracting more attention. The findings of this study suggest that the most promising results belong to the applications of hybrid ML methods, where a set of methods are combined to build a more powerful model.

Suggested Citation

  • Thuy Linh Jenny Phan & Ingolf Gehrhardt & David Heik & Fouad Bahrpeyma & Dirk Reichelt, 2022. "A Systematic Mapping Study on Machine Learning Techniques Applied for Condition Monitoring and Predictive Maintenance in the Manufacturing Sector," Logistics, MDPI, vol. 6(2), pages 1-22, May.
    • Handle: RePEc:gam:jlogis:v:6:y:2022:i:2:p:35-:d:826410
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    References listed on IDEAS

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    1. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    2. Gabriel Michau & Yang Hu & Thomas Palmé & Olga Fink, 2020. "Feature learning for fault detection in high-dimensional condition monitoring signals," Journal of Risk and Reliability, , vol. 234(1), pages 104-115, February.
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