收藏切换
Prediction Method for IGBT Aging Parameter Based on Transfer Learning Model
收藏切换
Wei LUO, Yufan LIU, Dewen ZHANG
Journal of Power Supply | 2026, 24(6) : 301 - 310
Less
收藏切换
Journal of Power Supply | 2026, 24(6): 301-310
• AI and Power Electronics •
Prediction Method for IGBT Aging Parameter Based on Transfer Learning Model
Full
Wei LUO, Yufan LIU, Dewen ZHANG
Affiliations
  • School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Published: 2026-06-30 doi: 10.13234/j.issn.2095-2805.2026.6.301
Outline
收藏切换

Aimed at the health status of insulated gate bipolar transistors (IGBTs) and the problem of evaluation on their remaining useful life (RUL), the aging parameter of IGBTs was predicted based on an adaptive recurrent neural network (AdaRNN) transfer learning model. First, the failure mechanism of IGBTs was analyzed, and the on-state collector-emitter voltage was selected as the aging parameter to evaluate the health status of IGBTs. Then, an IGBT aging test bench was set up to collect the aging parameter online. Finally, based on the acquired aging parameter, the AdaRNN model was used to predict the IGBT aging parameter. Results show that when the same IGBT aging parameter was used as the training and test sets, the accuracy and speed of prediction results of the traditional time series model and AdaRNN model were similar. When the aging parameters of two sets of different IGBTs were used as the training and test sets, the AdaRNN model can solve the problem of aging parameter difference caused by device difference, and the prediction accuracy was obviously higher than that of the traditional time series model, indicating the strong generalization capability and robustness of the new model.

Insulated gate bipolar transistor (IGBT)  /  data-driven  /  aging parameter  /  transfer learning
罗韡, 刘宇凡, 张德稳. 基于迁移学习模型的IGBT老化参数预测方法. 电源学报, 2026 , 24 (6) : 301 -310 . DOI: 10.13234/j.issn.2095-2805.2026.6.301
Wei LUO, Yufan LIU, Dewen ZHANG. Prediction Method for IGBT Aging Parameter Based on Transfer Learning Model[J]. Journal of Power Supply, 2026 , 24 (6) : 301 -310 . DOI: 10.13234/j.issn.2095-2805.2026.6.301
Year 2026 volume 24 Issue 6
40
4
Cite this Article
BibTeX
Article Info
doi: 10.13234/j.issn.2095-2805.2026.6.301
  • Receive Date:2024-03-03
  • Online Date:2026-10-29
  • Published:2026-06-30
Article Data
Affiliations
History
  • Received:2024-03-03
  • Revised:2024-06-22
  • Accepted:2024-08-02
Affiliations
    School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
References
Share
https://castjournals.cast.org.cn/joweb/dyxb/EN/10.13234/j.issn.2095-2805.2026.6.301
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT