The increasing complexity of electrical supply networks, combined with the growing demand for reliability, operational continuity, and asset longevity, has accelerated the development of computational approaches for equipment health assessment. Traditional maintenance strategies based on fixed schedules or reactive interventions are increasingly insufficient for modern power systems, where equipment failures may lead to significant economic losses, safety risks, and service interruptions. This research paper presents a comprehensive analytical framework for computational methods applied to equipment health assessment in electrical supply networks, integrating concepts from prognostics and health management (PHM), machine learning-based prediction, condition monitoring, data-driven diagnosis, and intelligent maintenance decision systems.

The study examines the theoretical foundations and practical implementation of computational health assessment methodologies, including sensor-based monitoring, feature extraction, statistical analysis, artificial intelligence models, and predictive maintenance architectures. The research synthesizes existing developments in PHM technology and applies these concepts to electrical supply infrastructure, where transformers, switchgear, transmission components, and distribution equipment require continuous evaluation of operational conditions. The methodology explores how computational models transform equipment condition data into actionable health indicators, enabling early fault detection and remaining useful life estimation.

The findings indicate that data-driven computational approaches provide significant improvements over conventional maintenance techniques by enabling dynamic assessment of equipment degradation patterns and supporting proactive maintenance decisions. Machine learning algorithms, integrated with real-time monitoring systems, enhance diagnostic accuracy by identifying hidden relationships between operational parameters and failure mechanisms. Recent studies demonstrate that predictive maintenance approaches based on machine learning can improve power system reliability by reducing unexpected failures and optimizing maintenance resources (Philip, 2025). However, challenges remain regarding data quality, model interpretability, cybersecurity, computational requirements, and adaptation to diverse operating environments.

The research contributes a structured understanding of computational health assessment for electrical supply networks by connecting PHM principles with modern intelligent computing techniques. The proposed perspective highlights the importance of integrating multi-source data acquisition, advanced analytics, and decision-support mechanisms to achieve sustainable and reliable power infrastructure management. Future developments are expected to focus on hybrid artificial intelligence models, digital twins, edge computing, and autonomous maintenance systems to improve the accuracy and scalability of equipment health evaluation.