Developing a Comprehensive Predictive Maintenance Model to Improve Lifecycle Management of Energy Sector Assets

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@article{1704872,
      author = {Chinedum Oscar Okuh, Emmanuella Onyinye Nwulu, Elemele Ogu, Peter Ifechukwude Egbumokei, Ikiomoworio Nicholas Dienagha; Wags Numoipiri Digitemie},
      title = {Developing a Comprehensive Predictive Maintenance Model to Improve Lifecycle Management of Energy Sector Assets},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {1},
      pages = {687-701},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704872.pdf},
      abstract = {This paper proposes developing a comprehensive predictive maintenance (PM) model to optimize asset lifecycle management in the energy sector. Predictive maintenance, which combines machine learning, data analytics, and the Internet of Things (IoT), has emerged as a transformative solution to mitigate unplanned downtime, reduce maintenance costs, and improve the reliability and longevity of energy assets. More efficient and proactive maintenance strategies are critical as the energy sector increasingly relies on complex infrastructures. This research highlights the limitations of traditional maintenance practices and introduces a robust PM model capable of predicting equipment failures before they occur, enabling preemptive actions that extend asset life and optimize operational performance. Through a case study on wind turbines, the paper illustrates the positive impacts of predictive maintenance, such as enhanced asset reliability, cost savings, and improved decision-making capabilities. The paper also explores key challenges in implementing predictive maintenance, including data quality, integration with legacy systems, and scalability. Further, it provides actionable recommendations for energy industry stakeholders to adopt and optimize predictive maintenance systems and future research directions to advance predictive capabilities and address current limitations. Integrating predictive maintenance in the energy sector can significantly contribute to sustainability, cost-effectiveness, and safety.},
      keywords = {Predictive Maintenance, Asset Lifecycle Management, Energy Sector, Machine Learning, Data Analytics, Internet of Things (IoT)},
      month = {July},
  }