Integrated Learning‐Based Ageing Assessment of Silicone Rubber

The surface ageing of silicone rubber composite insulators, widely used in power systems, poses significant challenges. This study integrates Fourier transform infrared (FTIR) spectroscopy with machine learning to evaluate ageing states and explore underlying mechanisms under various environmental conditions. A dataset covering light, medium, and severe ageing was built through FTIR experiments, spectral feature extraction, and data augmentation. An ensemble learning model achieved a classification accuracy of 95.42%. SHapley Additive exPlanations (SHAP) analysis indicated that silicon–oxygen backbones, silylmethyl groups, and hydroxyl groups are key to the ageing process. The silicon–oxygen backbone is dominant in initial oxidation and cross‐linking, whereas silylmethyl group reactions occur later. Hydroxyl group changes are complex and strongly environment‐dependent during severe ageing. The model was also applied to naturally aged samples from Tibet and Inner Mongolia, showing strong classification performance and revealing clear regional differences. These findings are valuable for assessing surface ageing, analysing ageing mechanisms and developing grading standards for composite insulators.