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February 8, 2026Open Access

Intelligent Digital Twin Framework for Real-Time Structural Health Monitoring and Optimization of Mechanical Systems Using Reinforcement Learning

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Authors

SSSantosh Kumar SAmerican Society of Civil EngineersVNVeena G NREVA UniversityKNKavitha B NThepsatri Rajabhat University

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Implication

This framework improves structural health monitoring in mechanical systems, suggesting efficient maintenance strategies.

Key Points

  • The aim is to develop a framework for real-time structural health monitoring and optimization of mechanical systems using reinforcement learning.
  • Integrated high-frequency sensor data with physics-based digital model.
  • Preprocessed sensor inputs from vibration, strain, and temperature for damage-sensitive features.
  • Applied Kalman filtering for precise state estimation.
  • Implemented anomaly detection for identifying faults.
  • Used reinforcement learning to optimize maintenance and control strategies.
  • Achieved 96.8% detection accuracy in identifying structural faults.
  • Obtained 95.6% precision and 96.1% recall metrics for anomaly detection.
  • Demonstrated a 14.7% optimization gain in operational costs.
  • Reduced response latency to 135 milliseconds compared to conventional methods.

Cite This Study

S et al. (2026) studied this question.

synapsesocial.com/papers/698828210fc35cd7a88475e1https://doi.org/10.1051/e3sconf/202669203001/pdf
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