A Review of Reliability Assessment and Lifetime Prediction Methods for Electrical Machine Insulation Under Thermal Aging

The thermal aging of insulation systems in electrical machines is a critical factor influencing their reliability and lifetime, particularly in modern high-performance electrical equipment. However, evaluating and predicting insulation lifetime under thermal aging poses significant challenges due to the complex aging mechanisms. Thermal aging not only leads to the degradation of macroscopic properties such as dielectric strength and breakdown voltage but also causes progressive changes in the microstructure, making the correlation between aging stress and aging indicators fundamental for lifetime evaluation and prediction. This review first summarizes the performance indicators reflecting insulation thermal aging. Subsequently, it systematically reviews current methods for reliability assessment and lifetime prediction in the thermal aging process of electrical machine insulation, with a focus on the application of different modeling approaches such as physics-of-failure (PoF) models, data-driven models, and stochastic process models in insulation lifetime modeling. The theoretical foundations, modeling processes, advantages, and limitations of each method are discussed. In particular, PoF-based models provide an in-depth understanding of degradation mechanisms to predict lifetime, but the major challenge remains in dealing with complex failure mechanisms that are not well understood. Data-driven methods, such as artificial intelligence or curve-fitting techniques, offer precise predictions of complex nonlinear relationships. However, their dependence on high-quality data and the lack of interpretability remain limiting factors. Stochastic process models, based on Wiener or Gamma processes, exhibit clear advantages in addressing the randomness and uncertainty in degradation processes, but their applicability in real-world complex operating conditions requires further research and validation. Furthermore, the potential applications of thermal lifetime models, such as electrical machine design optimization, fault prognosis, health management, and standard development are reviewed. Finally, future research directions are proposed, highlighting opportunities for breakthroughs in model coupling, multi-physical field analysis, and digital twin technology. These insights aim to provide a scientific basis for insulation reliability studies and lay the groundwork for developing efficient lifetime prediction tools.