Thermal Response Mechanisms and Quantitative Analysis of Defects in Multi-Material Power Equipment Based on Infrared Thermography

Multi-material structures such as carbon fiber reinforced polymer (CFRP) and epoxy resin are increasingly used in modern power equipment. However, significant differences in their thermophysical properties result in distinct defect thermal responses, which can reduce the reliability of infrared thermography inspections. To address this issue, this study investigates the thermal response mechanisms and quantitative analysis of defects in multi-material power equipment through finite element simulation and experimental validation. Three-dimensional transient heat transfer models containing air voids and heterogeneous insert defects were established using COMSOL Multiphysics for both carbon fiber reinforced polymer and epoxy resin matrices. Pulsed infrared thermography experiments were subsequently conducted to verify the simulation results. The effects of material properties, defect geometry, and cover-layer thickness on thermal response characteristics were systematically analyzed. The results show that thermal diffusivity is the key factor governing defect signal evolution. Carbon fiber reinforced polymer exhibits rapid thermal propagation and early transient responses, whereas epoxy resin produces delayed and slowly increasing thermal signals. Greater defect depth weakens thermal contrast and delays peak response time, while larger defect diameters enhance defect detectability. Increasing cover-layer thickness significantly attenuates defect signals and reduces imaging contrast. Experimental results are in good agreement with simulation predictions, confirming the validity of the proposed models. This work provides a quantitative analysis of defect thermal behavior in multi-material systems and offers a theoretical basis for adaptive infrared thermography inspection and condition assessment of power equipment. It should be noted that this study focuses on mechanistic understanding and parametric analysis rather than on proposing a dedicated quantitative defect-sizing or inversion method.