Digital Twin-Driven Predictive Maintenance and Remaining Useful Life Estimation for Complex Industrial Equipment Using Multisensor Data Fusion
Keywords:
digital twin, predictive maintenance, remaining useful life, multisensor data fusion, industrial internet of things, cyber-physical systems, edge-fog-cloud architecture, sustainability, fairness, governanceAbstract
The integration of digital twin technology with predictive maintenance and remaining useful life estimation has emerged as a transformational paradigm for managing complex industrial equipment. This paper presents a comprehensive systems-level examination of the architectures, data fusion strategies, and infrastructural considerations necessary to deploy digital twin-driven predictive maintenance at scale. The discussion foregrounds structural trade-offs between model fidelity and computational latency, examines the orchestration of heterogeneous multisensor data streams within a unified virtual representation, and explores how remaining useful life estimation can be embedded into the digital twin lifecycle to enable condition-based decision making. Particular emphasis is placed on governance, trust, policy implications, sustainability, robustness, and fairness as cross-cutting dimensions that are often underrepresented in purely technical accounts. The paper does not present new algorithms; instead, it synthesizes and critically analyzes the evolving landscape of cyber-physical infrastructures, edge-fog-cloud hierarchies, semantic interoperability frameworks, and responsible artificial intelligence practices. Through extended conceptual analysis and system-level case illustrations drawn from rotating machinery, energy systems, and manufacturing lines, the paper identifies the essential requirements for a sustainable and equitable predictive maintenance ecosystem. The conclusion outlines future directions including federated learning across asset fleets, explainable prognostics, and policy mechanisms that can reconcile autonomy with human oversight in high-stakes industrial environments.
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