电子科技2024，Vol.37Issue(8)：60-67,8.DOI:10.16180/j.cnki.issn1007-7820.2024.08.009
基于CSSA-LSTM的IGBT模块退化趋势预测
Prediction of Degradation Trend of IGBT Modules Based on CSSA-LSTM
摘要
Abstract
In view of the problem of high failure efficiency of IGBT(Insulated Gate Bipolar Transistor)modules in inverters,which are most prone to damage and aging,and the device degradation process is difficult to predict,a neural network prediction model combining LSTM(Long Short-Term Memory)and chaotic sparrow is proposed.By introducing the two-dimensional Pearson correlation coefficient method to obtain the combined degradation features,the LSTM-based voltage degradation prediction model is constructed.The model is used to adaptively extract the in-ternal correlations of degradation features to realize the screening of key information and digging deep degradation fea-tures.In the feasible domain of sparrow search algorithm,Gaussian random numbers with normal distribution and chaotic sequence corresponding to Tent mapping are introduced to improve the accuracy and stability of prediction.The learning rate,number of neurons and batch-size of the model are optimized to find the optimal value to match the network topology.The LSTM with the optimal structural parameters is used to predict each original data separately and obtain the final degradation prediction value.The accelerated degradation data set of NANS experimental center is analyzed and compared with the conventional prediction algorithm to verify the effectiveness and accuracy of the proposed algorithm.关键词
混沌麻雀搜索算法/LSTM/参数优化/退化趋势预测/IGBT/高斯变异/预测模型/Tent映射Key words
chaos sparrow search algorithm/LSTM/parameter optimization/prediction of degradation trends/IGBT/Gaussian variation/predictive models/Tent mapping分类
信息技术与安全科学引用本文复制引用
柳行青,赵国帅,韩素敏..基于CSSA-LSTM的IGBT模块退化趋势预测[J].电子科技,2024,37(8):60-67,8.基金项目
河南省科技攻关项目(202102210094) （202102210094）
国家重点研发计划(2016YFC0600906)Henan Provincial Science and Technology Research Project(202102210094) （2016YFC0600906）
National Key R&D Program Special Grant(2016YFC0600906) （2016YFC0600906）
