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...
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2025-01-01
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| author | Jian Zhang Jiajin Wang Hongbo Li Qin Zhang Xiangning He Cui Meng Xiaoyan Huang Youtong Fang Jianwei Wu |
| author_facet | Jian Zhang Jiajin Wang Hongbo Li Qin Zhang Xiangning He Cui Meng Xiaoyan Huang Youtong Fang Jianwei Wu |
| author_sort | Jian Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e1d38cf54ff74dadb2f8796475a8eeb6 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e1d38cf54ff74dadb2f8796475a8eeb62025-08-20T02:12:38ZengMDPI AGEnergies1996-10732025-01-0118357610.3390/en18030576A Review of Reliability Assessment and Lifetime Prediction Methods for Electrical Machine Insulation Under Thermal AgingJian Zhang0Jiajin Wang1Hongbo Li2Qin Zhang3Xiangning He4Cui Meng5Xiaoyan Huang6Youtong Fang7Jianwei Wu8State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaZhejiang Guoli Security Technology Co., Ltd., Hangzhou 310053, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaSchool of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaThe 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.https://www.mdpi.com/1996-1073/18/3/576thermal agingphysics of failureartificial intelligence algorithmscurve-fitting technologiesstochastic processapplication of insulation lifetime model |
| spellingShingle | Jian Zhang Jiajin Wang Hongbo Li Qin Zhang Xiangning He Cui Meng Xiaoyan Huang Youtong Fang Jianwei Wu A Review of Reliability Assessment and Lifetime Prediction Methods for Electrical Machine Insulation Under Thermal Aging Energies thermal aging physics of failure artificial intelligence algorithms curve-fitting technologies stochastic process application of insulation lifetime model |
| title | A Review of Reliability Assessment and Lifetime Prediction Methods for Electrical Machine Insulation Under Thermal Aging |
| title_full | A Review of Reliability Assessment and Lifetime Prediction Methods for Electrical Machine Insulation Under Thermal Aging |
| title_fullStr | A Review of Reliability Assessment and Lifetime Prediction Methods for Electrical Machine Insulation Under Thermal Aging |
| title_full_unstemmed | A Review of Reliability Assessment and Lifetime Prediction Methods for Electrical Machine Insulation Under Thermal Aging |
| title_short | A Review of Reliability Assessment and Lifetime Prediction Methods for Electrical Machine Insulation Under Thermal Aging |
| title_sort | review of reliability assessment and lifetime prediction methods for electrical machine insulation under thermal aging |
| topic | thermal aging physics of failure artificial intelligence algorithms curve-fitting technologies stochastic process application of insulation lifetime model |
| url | https://www.mdpi.com/1996-1073/18/3/576 |
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