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模型与数据混合驱动的IGBT寿命预测方法

IGBT life prediction method driven by model and data
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摘要 作为航空逆变器的关键模块,绝缘栅双极晶体管(IGBT)对其安全可靠起着决定性作用。考虑到航空逆变器运行工况复杂,且IGBT是最易失效的器件之一,通过分析航空逆变器IGBT的失效机理和关键特征参数,结合物理解析模型提出一种基于长短时记忆(LSTM)网络的IGBT寿命预测方法。建立IGBT状态监测数据与结温之间的关系,并由物理解析模型得到IGBT的累积损伤,实现IGBT的实时寿命预测。最后,利用NASA PCoE中心提供的IGBT加速老化实验数据集,开展寿命预测模型的验证,结果表明LSTM网络结合累积损伤模型能够有效预测IGBT的寿命,从而有助于提高航空逆变器的可靠性,降低日常维护成本。 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.
作者 田贵双 王少萍 石健 陶模 TIAN Guishuang;WANG Shaoping;SHI Jian;TAO Mo(School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China;State Key Laboratory of Marine Thermal Energy and Power,Wuhan 430205,China;Wuhan Second Ship Design and Research Institute,Wuhan 430205,China)
出处 《航空学报》 EI CAS CSCD 北大核心 2024年第15期38-49,共12页 Acta Aeronautica et Astronautica Sinica
基金 北京市自然科学基金-丰台轨道交通前沿研究联合基金(L221008) 热能动力技术重点实验室开放基金(TPL2022C02)。
关键词 航空逆变器 绝缘栅双极晶体管(IGBT) 长短时记忆(LSTM)网络 寿命预测 累积损伤 aviation inverter Insulated Gate Bipolar Transistor(IGBT) Long Short-Term Memory(LSTM)network life prediction cumulative damage
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