基于优化长短期记忆神经网络的IGBT寿命预测模型  

IGBT Lifetime Prediction Model Based on Optimized Long Short-Term Memory Neural Network

作　　者：任宏宇 余瑶怡 杜雄[1] 刘俊良 周君洁 Ren Hongyu;Yu Yaoyi;Du Xiong;Liu Junliang;Zhou Junjie(State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing University,Chongqing 400044 China)

机构地区：[1]输配电装备及系统安全与新技术国家重点实验室(重庆大学),重庆400044

出　　处：《电工技术学报》2024年第4期1074-1086,共13页Transactions of China Electrotechnical Society

基　　金：国家杰出青年科学基金(52125704);中央高校基本科研业务费专项资金(2022CDJHLW009)资助项目。

摘　　要：为了防止绝缘栅双极型晶体管(IGBT)突发性失效而影响电力电子设备安全可靠运行,急需对IGBT剩余寿命做出精确预测,这对现有预测模型在高准确性和低不确定性方面提出了挑战。该文提出一种优化模型,该模型通过利用逐次变分模态分解(SVMD)技术来提取退化特征,并采用贝叶斯方法优化长短期记忆(LSTM)神经网络的超参数以提高预测性能。首先,该模型通过SVMD技术将退化特征数据分解为多个模态后将有用模态重构从而提取和增强退化特征;其次;利用贝叶斯优化方法通过高斯过程(GP)代理模型和期望改进(EI)采集函数对LSTM预测模型超参数实现全局寻优;最后,基于SVMD特征提取技术和贝叶斯优化LSTM网络的预测模型通过实际IGBT退化特征数据证明了模型的有效性和优越性。结果表明,所提模型与传统优化模型相比,提高了13%的寿命预测准确性,并减少了34%的预测不确定性。Insulated gate bipolar transistors(IGBTs)are the core components of power electronic systems for converting and controlling electrical energy.However,the reliability of IGBT is lower than expected due to the complex environment and operating conditions,and the sudden failure of IGBT will lead to unplanned downtime of the entire system.Therefore,assessing the remaining useful lifetime(RUL)of IGBT will help guide regular maintenance and reduce economic losses.To prevent the sudden failure of IGBT,it is urgent to accurately predict the RUL of IGBT,but most existing methods have low prediction accuracy and high uncertainty.Therefore,this paper proposes an IGBT life prediction model based on optimized long short-term memory(LSTM).Starting from the two cores of the data-driven model,“data”and“model”are optimized and upgraded,which can effectively improve the accuracy and reduce the uncertainty of the model prediction.Firstly,the original condition monitoring(CM)data often contain many contaminated data that appear abnormal due to environmental interference and limitations of measurement technology.Meanwhile,CM data may also appear abnormal when IGBT devices degrade or fail,containing important information to characterize the degradation and failure of IGBT.It cannot be processed simultaneously with contaminated data.The proposed model extracts and enhances degraded features by decomposing the IGBT degraded data into multiple modes using the successive variational mode decomposition(SVMD)technique and then reconstructing the useful modes.Secondly,selecting the model’s hyperparameters will greatly affect the model’s learning ability and training effect.Traditionally,the selection of hyperparameters by the empirical trial-and-error method has contingency and randomness,seriously affecting the performance of the model.The proposed model uses the Bayesian optimization(BO)method to realize the global optimization of multiple hyperparameters in the model through the Gaussian process(GP)proxy model and expectation

关 键 词：IGBT 可靠性 寿命预测 模态分解 失效分布 

分 类 号：TN322.8[电子电信—物理电子学] TM46[电气工程—电器]