Digital Twin-Driven Knowledge-Enhanced AI Agents for Autonomous Operation and Maintenance Optimization in Industrial Cyber-Physical Systems
Keywords:
Digital twin; knowledge-enhanced AI; autonomous agents; operation and maintenance optimization; industrial cyber-physical systems; predictive maintenance; system governance; Industry 4.0Abstract
The increasing complexity and scale of industrial cyber-physical systems demand radical advances in operation and maintenance (O&M) paradigms. This paper presents a systemic analysis of digital twin-driven, knowledge-enhanced artificial intelligence agents designed to achieve autonomous O&M optimization. It argues that marrying high-fidelity digital twins with hybrid knowledge representations and deliberative agent architectures can transform reactive maintenance into self-adaptive, lifelong optimization. The discussion is anchored in a multi-layered architectural vision that integrates real-time asset mirroring, semantic knowledge graphs, ontology-based reasoning, and large language model advisory capabilities. A thorough examination of structural trade-offs reveals tensions between model fidelity and computational tractability, between centralized governance and edge autonomy, and between exploration for adaptation and exploitation for reliability. The paper further explores cross-domain applicability, drawing on examples from discrete manufacturing, wind energy, and process industries to illustrate how knowledge-enhanced agents can orchestrate predictive diagnostics, resource allocation, and collaborative decision-making across fleets of heterogeneous equipment. Governance frameworks are examined through the lens of human-agent coordination, auditability, adversarial robustness, and fairness in maintenance resource distribution. Sustainability implications including energy-aware scheduling and circular lifecycle management are assessed alongside policy considerations related to standardization, regulatory compliance, and workforce transformation. The synthesis of these dimensions culminates in a set of design principles and research directions that prioritize explainability, scalable multi-agent coordination, and resilient data pipelines. By treating digital twins not merely as simulators but as continuously evolving semantic mirrors, the proposed agent paradigm offers a pathway toward zero-downtime, trustworthy, and self-governing industrial ecosystems.
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