Intelligent Nuclear Energy Systems: AI-Based Monitoring, Safety Assessment and Predictive Maintenance

Colby Moorberg

Intelligent Nuclear Energy Systems: AI-Based Monitoring, Safety Assessment and Predictive Maintenance

Author(s)	Colby Moorberg
Country	United States
Abstract	The nuclear energy sector is increasingly exploring artificial intelligence (AI), machine learning and advanced data analytics to improve plant monitoring, safety assessment, equipment reliability and predictive maintenance. Nuclear facilities generate large volumes of heterogeneous operational data from sensors, control systems, inspections and maintenance records, creating opportunities for data-driven approaches to complement established physics-based models and engineering practices. This paper examines the emerging role of AI in intelligent nuclear energy systems, focusing on real-time condition monitoring, anomaly detection, fault diagnosis, risk-informed safety assessment and predictive maintenance. Particular attention is given to machine-learning models, digital twins, sensor fusion, explainable AI, edge computing and hybrid physics-informed approaches. A conceptual framework is proposed in which plant data are integrated with physical models and AI analytics to identify abnormal behaviour, estimate equipment health and support maintenance decisions. The paper also considers challenges associated with data scarcity, rare-event prediction, model validation, uncertainty quantification, cybersecurity, regulatory acceptance and the consequences of AI failure in safety-critical environments. The analysis suggests that AI should primarily function as a decision-support and monitoring technology rather than an uncontrolled replacement for established nuclear safety systems. Hybrid architectures that combine validated physical models, deterministic safety principles and carefully verified AI models may provide the most credible pathway toward intelligent nuclear operations. Future research should focus on trustworthy AI, human–machine collaboration, explainability, robust validation, digital-twin development and secure deployment.
Keywords	Nuclear Energy, Artificial Intelligence, Machine Learning, Nuclear Safety, Predictive Maintenance, Condition Monitoring, Digital Twin, Fault Diagnosis, Explainable AI, Nuclear Power Plants .
Field	Engineering
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-02

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American Journal of Advanced Multidisciplinary Research and Innovation

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Intelligent Nuclear Energy Systems: AI-Based Monitoring, Safety Assessment and Predictive Maintenance

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