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AI-Driven Predictive Maintenance for Energy Infrastructure

Author

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  • Ibrahim Adeiza Ahmed

    (Department of Engineering Management & Systems Engineering, The George Washington University, Washington D.C)

  • Paul Boadu Asamoah

    (Department of Engineering Management & Systems Engineering, The George Washington University, Washington D.C)

Abstract

The growing complexity and critical importance of energy infrastructure necessitate the adoption of advanced maintenance strategies to ensure reliability, efficiency, and sustainability. Traditional maintenance approaches, such as reactive and preventive maintenance, have proven inadequate in addressing the challenges posed by modern energy systems, particularly with the integration of renewable energy sources. This research explores the potential of artificial intelligence (AI)-driven predictive maintenance (PdM) as a transformative solution for the energy sector. By leveraging historical maintenance records and real-time sensor data, AI models, including machine learning and deep learning techniques, were developed to predict equipment failures with high accuracy.

Suggested Citation

  • Ibrahim Adeiza Ahmed & Paul Boadu Asamoah, 2024. "AI-Driven Predictive Maintenance for Energy Infrastructure," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(9), pages 507-528, September.
    • Handle: RePEc:bjc:journl:v:11:y:2024:i:9:p:507-528
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