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A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0

Author

Listed:
  • Jose E. Naranjo

    (Faculty of Systems, Electronic, and Industrial Engineering, Universidad Tecnica de Ambato, UTA, Ambato 180103, Ecuador
    These authors contributed equally to this work.)

  • Gustavo Caiza

    (Electronics and Automation Career, Universidad Politecnica Salesiana, UPS, Quito 170146, Ecuador
    These authors contributed equally to this work.)

  • Rommel Velastegui

    (Faculty of Systems, Electronic, and Industrial Engineering, Universidad Tecnica de Ambato, UTA, Ambato 180103, Ecuador
    These authors contributed equally to this work.)

  • Maritza Castro

    (Faculty of Systems, Electronic, and Industrial Engineering, Universidad Tecnica de Ambato, UTA, Ambato 180103, Ecuador
    These authors contributed equally to this work.)

  • Andrea Alarcon-Ortiz

    (Instituto Superior Tecnológico Tungurahua, ISTT, Ambato 180108, Ecuador
    These authors contributed equally to this work.)

  • Marcelo V. Garcia

    (Faculty of Systems, Electronic, and Industrial Engineering, Universidad Tecnica de Ambato, UTA, Ambato 180103, Ecuador
    Department of Systems Engineering and Automation, University of the Basque Country, EHU/UPV, 48013 Bilbao, Spain)

Abstract

The fourth industrial revolution was a milestone at the industrial level. It forced most industries to evolve technically and for their collaborators to prepare and advance together with technology; the oil industry was no exception. It develops its activities in dangerous and dynamic environments and needs to protect its human resources, equipment, and infrastructure. This article presents a scoping review, based on the PRISMA guidelines, of pipeline maintenance methodologies based on Industry 4.0. From the first collection of 123 articles from prestigious databases such as SpringerLink, MDPI, Scopus, IEEEXplore and ACM, a final sample of 31 articles was obtained. Here, technologies that enhance preventive and predictive maintenance systems are discussed. The results show that predictive maintenance compared to preventive maintenance has a percentage difference in upkeep time optimization of 38% in the last five years. This difference was corroborated with a Student’s t -test for independent samples, with a significance of 0.023. Likewise, the most used technologies were analyzed, with artificial intelligence standing out with 45.16%.

Suggested Citation

  • Jose E. Naranjo & Gustavo Caiza & Rommel Velastegui & Maritza Castro & Andrea Alarcon-Ortiz & Marcelo V. Garcia, 2022. "A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0," Sustainability, MDPI, vol. 14(24), pages 1-22, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16723-:d:1002405
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    References listed on IDEAS

    as
    1. Zheng, Ting & Ardolino, Marco & Bacchetti, Andrea & Perona, Marco, 2021. "The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 129469, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Ting Zheng & Marco Ardolino & Andrea Bacchetti & Marco Perona, 2021. "The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 59(6), pages 1922-1954, March.
    3. Jingyi Zhao & Chunhai Gao & Tao Tang, 2022. "A Review of Sustainable Maintenance Strategies for Single Component and Multicomponent Equipment," Sustainability, MDPI, vol. 14(5), pages 1-22, March.
    4. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    5. Li, Xinhong & Jia, Ruichao & Zhang, Renren & Yang, Shangyu & Chen, Guoming, 2022. "A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
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