USE OF AI IN PREDICTIVE MAINTENANCE OF POWER PLANTS

Authors

  • Syed Imran Hussain Department of Computer Science, COMSATS University Islamabad, Pakistan.
  • Hamza Rauf Siddiqui Power Systems Research Group, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Pakistan.

DOI:

https://doi.org/10.66238/ijrss.v5i1.50

Keywords:

Predictive Maintenance, Artificial Intelligence, Power Plants, Fault Diagnosis

Abstract

Predictive maintenance (PdM) in power plants is gaining prominence as a key approach to enhance operational efficiency, reduce downtime, and lower maintenance costs. The integration of Artificial Intelligence (AI) techniques such as machine learning, deep learning, and data analytics enables early detection of equipment faults and anomaly prediction. This paper presents a comprehensive overview of AI applications in predictive maintenance for power plants, emphasizing methodologies, challenges, and the potential for implementation within Pakistan’s energy sector. We analyze various AI models for condition monitoring, fault diagnosis, and remaining useful life (RUL) estimation. The study highlights case studies, data-driven AI frameworks, and the benefits of predictive maintenance in enhancing power plant reliability and sustainability.

References

Ahmad, N. R. (2025). Exploring the role of digital technologies in enhancing supply chain efficiency: A case study of e-commerce companies. Indus Journal of Social Sciences, 3(1), 226–237. https://doi.org/10.59075/ijss.v3i1.618

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Published

2025-06-30

How to Cite

Syed Imran Hussain, & Hamza Rauf Siddiqui. (2025). USE OF AI IN PREDICTIVE MAINTENANCE OF POWER PLANTS. INTERNATIONAL JOURNAL OF RESEARCH & SOCIAL SCIENCES, 5(1), 37–45. https://doi.org/10.66238/ijrss.v5i1.50

Issue

Vol. 5 No. 1 (2025)

Section

Articles

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Copyright (c) 2025 INTERNATIONAL JOURNAL OF RESEARCH & SOCIAL SCIENCES

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