The increasing complexity of electrical transmission and distribution networks has created a critical requirement for intelligent asset health evaluation mechanisms capable of improving reliability, operational efficiency, and lifecycle management. Traditional asset management practices primarily depend on periodic inspection, historical maintenance records, and experience-based decision-making, which are insufficient for modern power systems characterized by large-scale infrastructure, diverse equipment conditions, and rapidly changing operational environments. This research paper proposes an integrated conceptual framework for intelligent asset health evaluation by combining multidimensional asset indicators, lifecycle management principles, knowledge-driven evaluation mechanisms, and machine learning-based predictive maintenance approaches.
The study develops a research framework that considers physical asset condition, operational performance, economic value, environmental influence, and strategic importance as interconnected dimensions of asset health. Based on existing research on power grid physical asset evaluation, asset lifecycle management, and operational indicator knowledge systems, the paper analyzes the relationships among influencing factors affecting asset performance and proposes a structured evaluation approach. Interpretive structural modeling concepts are incorporated to understand hierarchical relationships among asset health determinants, while knowledge graph-based approaches are considered for improving information integration and decision support.
The research highlights that intelligent asset health evaluation should move beyond isolated equipment monitoring toward a comprehensive decision architecture integrating real-time data acquisition, condition assessment, predictive analytics, and strategic asset optimization. Machine learning-based predictive maintenance approaches demonstrate significant potential in identifying abnormal operational patterns, forecasting equipment degradation trends, and reducing unnecessary maintenance activities (Philip, 2025). However, practical implementation requires addressing challenges related to data quality, model interpretability, cybersecurity, and organizational adaptation.
The findings indicate that an intelligent asset health evaluation framework can enhance the sustainability and resilience of transmission and distribution networks by enabling proactive maintenance, improving investment decisions, and supporting lifecycle-oriented management strategies. The proposed framework provides theoretical and practical contributions by establishing a comprehensive perspective for evaluating power infrastructure health and supporting the transition from conventional maintenance models toward intelligent, predictive, and value-oriented asset management.

Intelligent asset health evaluation, power transmission and distribution networks, predictive maintenance, machine learning

Fu Chengran, He Yongxiu. Analysis of Influencing Factors of Benefit of Physical Assets of Substation Equipment Based on Interpretation Structure Model [J]. Shandong Electric Power Technology, 2019, 46 ( 05 ): 35–40.

Li Zhixing, Wan Lei, Zhou Hongyan, Wang Guangjie, Deng Lin. Construction and application of knowledge graph of power grid enterprise operation indicators [J]. Rural Electrification, 2020 ( 02 ): 43–46.

M. Iqbal, J. Ma, N. Ahmad, K. Hussain, M. S. Usmani. Promoting sustainable construction through energy-efficient technologies: an analysis of promotional strategies using interpretive structural modeling[J]. International Journal of Environmental Science and Technology, 2021 (prepublish).

Ma Ti, Shi Lei, Yue Yunli, Zhao Zheyuan, Li Yuan, Dong Fugui. Analysis on strategic objective architecture and influence mechanism of provincial power grid companies [J]. Zhejiang Electric Power, 2019, 38 ( 02 ): 15–21.

Philip, P. G. (2025). Predictive Maintenance Approach for Electric Power Systems Using Machine Learning. The American Journal of Interdisciplinary Innovations and Research, 7(09), 145–160. Retrieved from https://theamericanjournals.com/index.php/tajiir/article/view/ml-predictive-maintenance-power-systems

Tang Xiuying. Research on evaluation index of asset life cycle management system [J]. Low Carbon World, 2017 ( 32 ): 263–264.

Wei Pu, Li Xuyang, Liu Qiming. Construction of comprehensive evaluation index system for analyzing power grid physical assets [J]. Green Science and Technology, 2020 ( 24 ): 42–44.

Wu Han. Research on Industrial Ecosystem Construction and Health Evaluation of State Grid Corporation [D]. North China Electric Power University ( Beijing), 2018.