Acknowledgement
This work was supported by the National Natural Science Foundation of China, grant number U2267206 ; Research Foundation of Education Bureau of Hunan Province, grant number 22C0223 and 21B0434.
References
- P. Duan, S. Jie, L. Liang, Y. Xu, Y. Yuan, Research on position monitoring system of rod control rod in nuclear power station, Instrumentation 30 (2023) 62-66.
- G. Zheng, K. Huang, H. Yu, Q. Ma, Y. Jin, Y. Tian, G. Li, Power control equipment for control rod drive mechanisms based on IGBT in nuclear power plant, Nucl. Power Eng. 35 (2014) 138-141.
- Z. Zhong, Y. Wang, Y. Huang, D. Xiao, P. Xia, C. Liu, Remaining useful life prediction of IGBT modules across working conditions based on ProbSparse self-attention, J. Shanghai Jiaot. Univ. 57 (2023) 1005-1015, https://doi.org/10.16183/j.cnki.jsjtu.2021.538.
- Y. Shi, Y. Ai, S. Chen, C. Zhang, J. Liu, A health state prediction method of traction converter IGBT based on optimized particle filter, Microelectron. Reliab. 139 (2022), https://doi.org/10.1016/j.microrel.2022.114840.
- Z. Ni, X. Lyu, O.P. Yadav, B.N. Singh, S. Zheng, D. Cao, Overview of real-time lifetime prediction and extension for SiC power converters, IEEE Trans. Power Electron. 35 (2020) 7765-7794, https://doi.org/10.1109/TPEL.2019.2962503.
- J. Zhang, J. Hu, H. You, R. Jia, X. Wang, X. Zhang, A remaining useful life prediction method of IGBT based on online status data, Microelectron. Reliab. 121 (2021), https://doi.org/10.1016/j.microrel.2021.114124.
- C. Li, IGBT fault prediction combining terminal characteristics and artificial intelligence neural network, Comput. Math. Methods Med. 2022 (2022), https://doi.org/10.1155/2022/7459354.
- D. Yu, An improved prediction model of IGBT junction temperature based on backpropagation neural network and kalman filter, Complexity 2021 (2021), https://doi.org/10.1155/2021/5542889.
- B. Chen, G. Lu, H. Fang, M. Zhang, Y. Dong, Prediction method of IGBT health parameters based-on degradation data and DBN algorithm, Comput. Meas. Control 25 (2017) 71-75, https://doi.org/10.16526/j.cnki.11-4762/tp.2017.05.020.
- Z. Liu, C. Zhu, IGBT life prediction based on Elman neural network model, Semiconductor Technology 44 (2019) 395-400, https://doi.org/10.13290/j.cnki.bdtjs.2019.05.013.
- B. Li, Research on Fault Prediction of IGBT Based on Terminal Characteristics, Beijing Jiaotong University, 2021, https://doi.org/10.26944/d.cnki.gbfju.2021.001513.
- H. Ren, Y. Yu, X. Du, J. Liu, J. Zhou, IGBT Lifetime Prediction Model Based on Optimized Long Short-Term Memory Neural Network, vol. 39, Transactions of China Electrotechnical Society, 2024, pp. 1074-1086, https://doi.org/10.19595/j.cnki.1000-6753.tces.222231.
- J. Zhang, Z. Xue, H. Chen, P. Sun, C. Gao, Y. Duan, CNN-BiLSTM based on attention mechanism for prediction of lGBT remaining useful life, Semiconductor Technology 49 (4) (2024) 373-379, https://doi.org/10.13290/j.cnki.bdtjs.2024.04.010.
- D. Astigarraga, F.M. Ibanez, A. Galarza, J.M. Echeverria, I. Unanue, P. Baraldi, E. Zio, Analysis of the results of accelerated aging tests in insulated gate bipolar transistors, IEEE Trans. Power Electron. 31 (2016) 7953-7962, https://doi.org/10.1109/TPEL.2015.2512923.
- M. Ma, W. Guo, M. Zhan, S. Yang, F. Li, Reconfiguration modulation strategy analysis for proactive adaptability of the monitored inverter to on-line monitoring system of IGBT modules, Energy Rep. 6 (2020) 554-565, https://doi.org/10.1016/j.egyr.2020.11.189.
- R. Liu, H. Li, K. Yu, R. Yao, W. Lai, J. An, X. Wang, H. Li, Analysis of package condition monitoring method of Wire-bonded lGBT devices, J. Inst. Eng. Electr. Eng. Div. 15 (2022) 71-78+82, https://doi.org/10.19768/j.cnki.dgjs.2022.15.019.
- A. Ismail, L. Saidi, M. Sayadi, M. Benbouzid, A new data-driven approach for power IGBT remaining useful life estimation based on feature reduction technique and neural network, Electronics (Switzerland) 9 (2020) 1-15, https://doi.org/10.3390/electronics9101571.
- Q. Zhang, Y. Yang, P. Zhang, A novel online chip-related aging monitoring method for IGBTs based on the leakage current, IEEE Trans. Ind. Electron. 70 (2023) 2003-2014, https://doi.org/10.1109/TIE.2022.3163516.
- U.M. Choi, F. Blaabjerg, S. Jorgensen, S. Munk-Nielsen, B. Rannestad, Reliability improvement of power converters by means of condition monitoring of IGBT modules, IEEE Trans. Power Electron. 32 (2017) 7990-7997, https://doi.org/10.1109/TPEL.2016.2633578.
- G. Sonnenfeld, K. Goebel, J. Celaya, An agile accelerated aging, characterization and scenario simulation system for gate controlled power transistors, IEEE Autotestcon (2008) 208-215, https://doi.org/10.1109/AUTEST.2008.4662613.
- P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res. 11 (2010) 3371-3408.
- X. Yan, Y. Liu, M. Jia, Health condition identification for rolling bearing using a multi-domain indicator-based optimized stacked denoising autoencoder, Struct. Health Monit. 19 (2020) 1602-1626, https://doi.org/10.1177/1475921719893594.
- X. Wang, J. Wu, C. Liu, H. Yang, Y. Du, W. Niu, Exploring LSTM based recurrent neural network for failure time series prediction, J. Beijing Univ. Aeronaut. Astronaut. 44 (2018) 772-784, https://doi.org/10.13700/j.bh.1001-5965.2017.0285.
- Y. Shu, J. Jianguo, L. Junjie, L. Yunlong, Z. Zhongzheng, L. Cong, Fault diagnosis and tolerance control of five-level nested NPP converter using wavelet packet and LSTM, IEEE Trans. Power Electron. 35 (2020) 1907-1921, https://doi.org/10.1109/TPEL.2019.2921677.
- S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Software 69 (2014) 46-61, https://doi.org/10.1016/j.advengsoft.2013.12.007.
- A. Mahmoudi, I. Jlassi, A.J.M. Cardoso, K. Yahia, Model free predictive current control based on a grey wolf optimizer for synchronous reluctance motors, Electronics (Switzerland) 11 (2022), https://doi.org/10.3390/electronics11244166.
- Z. Jiang, Fault Diagnosis and Prediction of Analog Filter Based on Deep Learning, Harbin Institute of Technology, 2019, https://doi.org/10.27061/d.cnki.ghgdu.2019.002320.