@article{TIAN2024, 
author = {Guishuang TIAN and Shaoping WANG and Jian SHI and Mo TAO},
title = {IGBT life prediction method driven by model and data},
year = {2024},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {45},
number = {15},
pages = {630173},
keywords = {life prediction, Long Short-Term Memory (LSTM) network, aviation inverter, Insulated Gate Bipolar Transistor (IGBT), cumulative damage},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2024.30173},
doi = {10.7527/S1000-6893.2024.30173},
abstract = {As a key module of aviation inverter, Insulated Gate Bipolar Transistor (IGBT) plays a decisive role in its safety and reliability. Considering the complex operating conditions of aviation inverter and the fact that IGBT is one of the most vulnerable components for failure, this paper analyzes the failure mechanism and key characteristic parameters of IGBT in aviation inverter. Based on this, an IGBT life prediction method is proposed by combing Long Short-Term Memory (LSTM) network with physical analytical model. The relationship is established for IGBT between its state monitoring data and junction temperature, and the cumulative damage of IGBT is obtained from the physical model, so as to achieve the real-time life prediction of IGBT. Finally, the IGBT accelerated aging experimental dataset provided by the NASA PCoE Center is applied to validate the prediction model. The corresponding results show that the LSTM network combined with the cumulative damage model can effectively predict the lifespan of IGBT, thereby contributing to improving the reliability and reducing the daily maintenance cost of aviation inverters.}
}