Knowledge Graph-Based Engineering Asset Management for Predictive Decision Intelligence

  • Authors

    • Vladimir Glushkov Director, Institute of Cybernetics, Ukraine Author
    • Victor Glushkov Academician, Academy of Sciences of USSR, Ukraine Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2025PI1N4Z

    Published 02-03-2025

  • Knowledge Graph, Engineering Asset Management, Predictive Decision Intelligence, Predictive Maintenance, Artificial Intelligence, Industrial Internet Of Things (Iiot), Digital Twin, Semantic Web, Graph Database, Machine Learning, Asset Reliability, Industry 4.0

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    Knowledge Graph-Based Engineering Asset Management for Predictive Decision Intelligence. (2025). International Journal of Modern Research in Science & Engineering, 8(1), 01-18. https://doi.org/10.67228/30716357/IJMRSE-2025PI1N4Z

  • Abstract

    Engineering Asset Management (EAM) is evolving through Industry 4.0 technologies such as IoT, AI, Digital Twins, and cloud computing, which generate vast amounts of asset-related data. However, conventional asset management systems often fail to integrate heterogeneous data sources, resulting in fragmented information, reactive maintenance, and increased operational costs. This study proposes a Knowledge Graph-Based Engineering Asset Management (KG-EAM) framework to enhance predictive decision intelligence for smart industrial assets. The framework integrates data from Industrial IoT devices, SCADA systems, Enterprise Asset Management (EAM) platforms, Computerized Maintenance Management Systems (CMMS), and engineering documentation into a unified semantic knowledge ecosystem. By combining ontology modeling, graph databases, graph embeddings, machine learning, and explainable AI (XAI), the framework enables predictive maintenance, root cause analysis, asset health prediction, and risk-aware decision-making. The proposed approach improves asset reliability, maintenance planning, operational efficiency, knowledge sharing, and lifecycle management while reducing downtime, maintenance costs, and unexpected equipment failures across diverse industrial sectors.

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