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

作     者：Zhang, Jian Wang, Jiajin Li, Hongbo Zhang, Qin He, Xiangning Meng, Cui Huang, Xiaoyan Fang, Youtong Wu, Jianwei 

作者机构：Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Zhejiang Guoli Secur Technol Co Ltd Hangzhou 310059 Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou 310027 Peoples R China 

出 版 物：《ENERGIES》 (Energies)

年 卷 期：2025年第18卷第3期

页      面：576-576页

核心收录：

基　　金：Key Project of the State Key Laboratory of Fluid Power and Mechatronic Systems Key Research and Development Program of Zhejiang Province [2024C01140] Key Research and Development Program of Hangzhou [2024SZD1A18] Scientific Research Fund of Zhejiang University [XY2024016] SKLoFP_ZZ_2405 

主　　题：thermal aging physics of failure artificial intelligence algorithms curve-fitting technologies stochastic process application of insulation lifetime model 

摘      要：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