Case Studies of Digital Healthcare Engineering
Digital Healthcare Engineering (DHE): Transforming Infrastructure MaintenanceDigital Healthcare Engineering (DHE) represents a significant shift from theoretical concepts to real engineering implementation. It creates a closed-loop digital healthcare system where sensing, analysis, and repair actions work together to sustain structural integrity and environmental safety.I. Core Concepts of DHE ImplementationDHE systems rely heavily on Artificial Intelligence (AI) and the integration of multiple modules.
Case Study 1: LNG Infrastructure Health Monitoring
DHE integration into Liquefied Natural Gas (LNG) systems focuses on continuously tracking structural behavior, especially cryogenic effects.
• Sensing and Data: Sensors are embedded in the LNG tent or on the wall of the tank to continuously record structural and thermal behavior.
• Corrosion Factor: If the system has sufficient training data, DHE can detect corrosion factors and simulate the corrosion factor into its output predictions.• Benefits: The system continuously tracks cryogenic effects. It identifies potential cracks or leaks using prediction models and provides operators with early warnings. This helps reduce downtime, enhance safety, and extend the operational life of the tanks. The outcome clearly saves economic labor and production capability.
Case Study 2: Rehabilitating Corroded Subsea Pipelines
This DHE application focuses on managing the integrity of corroded subsea pipelines that have been strengthened using Fiber Reinforced Polymer (FRP) systems.
The Role of Artificial Intelligence (AI)
The "brain" of the DHE system heavily relies on advanced AI models, which are crucial for predictive analysis.
• Corrosion Classification: Algorithms like Artificial Neural Networks (ANN) analyze corrosion data to identify wall loss patterns (reduction of thickness) and early delamination of the FRP wrap.
• Time Series Prediction: Recurrent Neural Networks (RNN) and LSTM architectures study time series sensor data (e.g., pressure fluctuation, strain history, or temporary cycles). The purpose is to predict how corrosion will evolve over the next month or years.
• Continuous Calibration: These AI models are trained using both historical maintenance records and real-time sensor input. Their predictions are continuously recalibrated through the digital twin feedback loop.
• High Accuracy: Related hybrid frameworks have shown prediction accuracy for corrosion classification of 90% and above, with less than 10% error in estimating degradation rates.
• Informing Maintenance: The predictive analysis output directly informs rehabilitation planning, determining when an FRP overwrap should be reapplied, how thick it should be, and where secondary corrosion might be triggered by stress concentration