Abstract
In order to solve the problem of IGBT aging failure caused by the cyclic impact of thermal stress and electrical stress when working in a complex environment, a network model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) was proposed to predict the IGBT life. Collector - emitter turn-off transient voltage is selected as the failure characteristic parameter, and a CNN-LSTM hybrid model is built. In order to accelerate the training speed of the network, the activation function uses the ELU function, and the Adam algorithm is used to train the network, so as to realize the prediction of the failure characteristic parameter data. Through experimental comparison with other time series prediction models, it is verified that the hybrid model in this paper can better realize the IGBT life prediction, and also provides a certain reference value for the life prediction of other power electronic devices.
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Acknowledgements
This research has been supported by the Natural Science Foundation of Bei**g municipality, China (Grant No. 3212032).
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Liu, Q., Tong, Q., Wang, L., An, G. (2022). IGBT Life Prediction Based on CNN1D-LSTM Hybrid Model. In: Liang, J., Jia, L., Qin, Y., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021. EITRT 2021. Lecture Notes in Electrical Engineering, vol 867. Springer, Singapore. https://doi.org/10.1007/978-981-16-9909-2_66
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DOI: https://doi.org/10.1007/978-981-16-9909-2_66
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