A Digital Twin-Driven Framework for Predictive Maintenance in Complex Engineering Systems
Keywords:
Digital Twin; Predictive Maintenance; Complex Engineered Systems; System Architecture; Cyber-Physical Infrastructure; Sustainability; GovernanceAbstract
The increasing scale and interdependence of complex engineering systems, such as power grids, aerospace fleets, and advanced manufacturing lines, demand a fundamental shift from reactive and scheduled maintenance to predictive strategies capable of anticipating degradation and preventing failures in real time. This paper proposes a comprehensive digital twin-driven framework that integrates multi-layered system architecture, advanced data infrastructure, model governance, and lifecycle sustainability. Departing from narrowly focused technical implementations, the framework is examined through the lens of large-scale systems research, emphasizing structural trade-offs among model fidelity, data fidelity, computational cost, and operational latency. The analysis extends beyond pure engineering to encompass governance mechanisms, algorithmic fairness, policy implications, and the socio-technical resilience of maintenance ecosystems. A conceptual architecture is presented that couples physics-informed and data-driven hybrid twins with a continuous validation layer and a human-in-the-loop decision interface. Particular attention is paid to interoperability across heterogeneous asset fleets, the role of semantic standards, and the edge-cloud continuum that underpins scalable twin deployment. The paper further interrogates the ethical dimensions of predictive maintenance, including liability attribution when autonomous recommendations fail, and the risk of systemic bias against under-instrumented assets. Deployment pathways are assessed from both greenfield and brownfield perspectives, highlighting the tension between rapid digitalization and the preservation of legacy operational knowledge. By synthesizing cross-domain case illustrations from aviation, wind energy, and industrial robotics, the framework’s capacity to balance robustness, adaptability, and long-term economic and environmental sustainability is critically evaluated. The article closes with a forward-looking research agenda centered on federated twin ecosystems, generative artificial intelligence for scenario generation, and the establishment of international regulatory benchmarks for digital twin certification in safety-critical maintenance operations.
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