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作 者:仇志杰[1] 郑丹 范涛[1,2] 温旭辉 QIU Zhijie;ZHENG Dan;FAN Tao;WEN Xuhui(State Key Laboratory of High Density Electromagnetic Power and Systems,Institute of Electrical Engineering,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)
机构地区:[1]中国科学院电工研究所高密度电磁动力与系统全国重点实验室,北京100190 [2]中国科学院大学,北京100049
出 处:《电机与控制学报》2025年第2期105-115,共11页Electric Machines and Control
基 金:国家重点研发计划(2021YFB2500600)。
摘 要:为了实现动态变化工况下绝缘栅双极晶体管(IGBT)特定个体的实时可更新寿命预测,提出一种解析寿命模型与基于相似性的数据驱动模型融合的变应力工况剩余使用寿命预测方法。首先,通过恒定应力功率循环试验建立器件的一维解析寿命模型,基于解析寿命模型实现了不同应力工况下的损伤等效。随后,基于退化相似性实现了对特定个体退化轨迹的剩余寿命预测,并采用灰狼优化算法(GWO)对退化相似性模型参数进行优化。最终,通过一组变应力加速试验验证所提方法的有效性。结果表明,该方法能够实现变应力工况下基于器件个体退化状态的寿命预测,与现有解析寿命模型方法相比,显著提高了预测的准确性。To achieve real-time up-datable lifetime prediction of specific insulated gate bipolar transistor(IGBT)individuals under dynamically changing working conditions,a remaining useful life prediction method under variable stress conditions combining analytical lifetime model and data-driven model based on similarity was proposed.Firstly,a one-dimensional analytical lifetime model of the device was established through constant stress power cycling tests,and damage equivalence under different stress conditions was realized based on the analytical lifetime model.Subsequently,the remaining useful life prediction of specific individual degradation trajectories was achieved based on degradation similarity,and the parameters of the degradation similarity model were optimized using the grey wolf optimization(GWO)algorithm.Finally,effectiveness of the proposed method was verified through a set of variable stress accelerated tests.The results show that the method can achieve lifetime prediction based on the degradation state of individual devices under variable stress conditions and significantly improves the prediction accuracy compared with the existing analytical lifetime model methods.
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