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Predictive Maintenance in Industrial Internet of Things: Current Status

Author

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  • Sarah Chaudhry

    (Department of Computer Science, Bahria University Lahore Campus, Pakistan)

Abstract

Introduction/Importance of Study:Predictive Maintenance (PdM) is a key challenge within the Industrial Internet of Things (IIoT). It aims to enhance system operations by minimizing equipment failures, leading to smoother operations and increased productivity. By anticipating maintenance needs before failures occur, PdM ensures more reliable and efficient industrial processes. Novelty Statement:This study examines maintenance techniques and datasets that leverage AI and ML for predictive maintenance in the context of industrial IoT. The primary goal is to enhance productivity, identify faults before failures occur, and minimize downtime. By utilizing advanced algorithms, the study aims to improve the efficiency and reliability of industrial systems. Material and Method:A systematic literature review of state-of-the-art predictive maintenance in the context of industrial IoT, incorporating machine learning (ML) and artificial intelligence (AI) methods, is conducted. This review is based on research articles retrieved fromthe Dimensions.ai database, covering publications from 2018 to 2024.Result and Discussion:This comprehensive analysis offers valuable insights for advancing Predictive Maintenance (PdM) strategies in the Industrial Internet of Things (IIoT), ultimately contributing to more efficient manufacturing processes. The study highlights leading publication venues and top keywords in this research area, providing a clear picture of emerging trends. It also explores the prognosis of PdM within the manufacturing industry. Additionally, the review discusses relevant models, methods, input variables, and datasets in the PdM and IIoT domain, with a particular focus on machine learning (ML) and artificial intelligence (AI) techniques. Among the most widely used techniques for PdM in IIoT are deep learning, artificial neural networks, and random forest.Concluding Remarks:Subsequently, the study highlights various challenges, offering future research directions aimed at refining predictive maintenance techniques.

Suggested Citation

  • Sarah Chaudhry, 2024. "Predictive Maintenance in Industrial Internet of Things: Current Status," International Journal of Innovations in Science & Technology, 50sea, vol. 6(7), pages 32-44, October.
    • Handle: RePEc:abq:ijist1:v:6:y:2024:i:7:p:32-44
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    References listed on IDEAS

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    1. Bas van Oudenhoven & Philippe Van de Calseyde & Rob Basten & Evangelia Demerouti, 2023. "Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective," International Journal of Production Research, Taylor & Francis Journals, vol. 61(22), pages 7846-7865, November.
    2. Alberto Martín-Martín & Mike Thelwall & Enrique Orduna-Malea & Emilio Delgado López-Cózar, 2021. "Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 871-906, January.
    3. Donthu, Naveen & Kumar, Satish & Mukherjee, Debmalya & Pandey, Nitesh & Lim, Weng Marc, 2021. "How to conduct a bibliometric analysis: An overview and guidelines," Journal of Business Research, Elsevier, vol. 133(C), pages 285-296.
    4. Alberto Martín-Martín & Mike Thelwall & Enrique Orduna-Malea & Emilio Delgado López-Cózar, 2021. "Correction to: Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 907-908, January.
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