A Method to Measure Thermal Conductivity of Vacuum Insulation Panel Using Enhanced Extreme Learning Machine Model

Thermal conductivity is an important quantity which represents the characteristic of Vacuum Insulation Panel's (VIP's) performance. Precise measurement of thermal conductivity provides better quality assurance for the users. In this paper, we presented a novel embedded sensor method to measure the thermal conductivity of VIP. The proposed method evaluated the quality of VIP primarily based on the relationship between thermal conductivity and frequency characteristic of the output signal. In addition, we presented a new mean ridge regression extreme leaning machine (M-RRELM) model via improving extreme learning machine (ELM) by ridge regression to modify the relationship between the thermal conductivity and the output signal frequency characteristic. Experiments have shown that the M-RRELM model has higher precision compared with the traditional ELM. The proposed method achieved good performance and was faster than the well known methods.