Artificial Intelligence System Design Automation for Equipment Maintenance Decision-Making in Oil and Gas Industry

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@article{1717344,
      author = {Eboh Chamberline Ihekwoaba, G. N. Obunadike, Abah Joshu A.},
      title = {Artificial Intelligence System Design Automation for Equipment Maintenance Decision-Making in Oil and Gas Industry},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {1413-1429},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717344.pdf},
      abstract = {This paper has investigated how automation of artificial intelligence system design takes place in equipment’s maintenance decisions within the oil and gas sector. In the oil and gas industry, pumps, compressors, turbines, pipelines, drilling machines, storage tanks and refinery systems are necessary equipment that is vital. When these systems fail, it may cause a halt in production, losses and damages to the environment, safety risks, and decreased operational efficiency. Meanwhile traditional maintenance strategies like corrective and preventive maintenance are frequently not sufficient, since they are based on equivalence and predetermined maintenance programs, manual inspection, and actions taken after failures have already taken place. The research design was of qualitative research examining an organization of the literature on a systematic review of the scholarly works. Peer-reviewed journal articles, conference papers, books, and industrial reports on the topics of artificial intelligence, predictive maintenance, Internet of Things, digital twins, and oil and gas maintenance systems provided secondary data. The results have demonstrated that the most valuable technologies utilized in maintenance decision-making are machine learning, deep learning, predictive analytics, IoT, digital twins, and explainable AI since those enhance fault detection, equipment monitoring, predictive maintenance, and maintenance scheduling. The research also found that AI-based maintenance solutions are very common in the drilling process, pipeline inspection, refinery, offshore platforms, and gas processing plant. These technologies assist organizations to reduce downtimes, enhance equipment’s reliability, cut down on maintenance expenses, enhance safety, and enable real-time decisions. Nonetheless, the paper also established some obstacles to the implementation of AI technologies in the oil and gas maintenance systems, such as poor maintenance data, high implementation cost, security concerns (cyber), insufficient infrastructure, unskilled human resources, and resistance to change were also identified. In the study, it was concluded that there is a great potential in terms of automation in the design of artificial intelligence system that is applicable in the oil and gas industry with respect to maintenance decision making of equipment. It suggested more funding in predictive maintenance systems, digital monitoring, explainable AI, IoT infrastructure, and employee training to enhance the collaboration and efficiency of AI-enhanced maintenance systems in the industry.},
      keywords = {Artificial Intelligence, Predictive Maintenance, Oil And Gas Industry, Machine Learning, Deep Learning.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717344}
  }