Real-Time Service Life Estimation of Vacuum Insulated Panels via Embedded Sensing and Machine Learning Models

Although vacuum insulated panels (VIPs) are known for their exceptional thermal insulation capabilities, their service life is limited due to an increase in internal gas pressure and material aging. In this study, an innovative monitoring system incorporating embedded sensors was developed to estimate the lifespan of VIPs in real time. A test panel was specifically selected to degrade its thermal conductivity over a shortened timeframe to facilitate validation and optimize the experimental duration. Hourly pressure and temperature data collected from the sensors embedded within the panel were analyzed using established pressure–thermal conductivity (λ) relationships from the literature. Based on the time-dependent λ values, a machine learning model employing a random forest regressor was trained to predict the panel’s lifetime. The model demonstrated high accuracy with R² = 0.9999 and RMSE = 0.0017 mW/mK. During the test period, the panel maintained acceptable performance, and the model projected that the critical thermal conductivity threshold of 8.0 mW/mK would be reached at day 66.9. This approach enables continuous, in situ field monitoring of VIP service life without the need for laboratory infrastructure and offers a scalable and practical solution for assessing long-term energy efficiency.