电气传动2026，Vol.56Issue(4)：68-75,8.DOI:10.19457/j.1001-2095.dqcd26454
基于CNN-Attention-LSTM的IGBT键合线失效状态评估
Failure State Evaluation of IGBT Bonding Wire Based on CNN-Attention-LSTM
摘要
Abstract
The insulated gate bipolar transistor(IGBT),as the core device of power electronics system,is widely used in industrial control,transportation and new energy power generation due to its high efficiency and high switching frequency.However,the internal bonding wire is vulnerable to thermal stress and current shock during long-term operation,which has become one of the main reasons for IGBT module failure.A hybrid model combining convolutional neural network(CNN),attention mechanisms,and long short-term memory(LSTM)was proposed to accurately evaluate the health of bonding wire.Short-circuit current data were collected by cutting the bonding wire experiment,and the health state was divided into three categories:healthy,damaged and faulty based on the short-circuit current deviation.CNN was used to extract the local characteristics of the short-circuit current,and the attention mechanism focused on the abnormal change of the key time step.LSTM captured the time-sequence dependence of the short-circuit current,so as to realize the accurate classification of the failure state of the bonding wire.The results show that the model has high classification accuracy on verification set and can distinguish the different health states of bonding wire effectively.The research results provide scientific basis for health monitoring and failure diagnosis of IGBT module,and have important engineering application value.关键词
IGBT器件/键合线/卷积神经网络/长短期记忆网络/健康状态评估Key words
insulated gate bipolar transistor(IGBT)device/bonding wire/convolutional neural network(CNN)/long short-term memory(LSTM)network/health status assessment分类
信息技术与安全科学引用本文复制引用
胡翔政,甘培,李科,吴文奇,郭汉挺,黄先进..基于CNN-Attention-LSTM的IGBT键合线失效状态评估[J].电气传动,2026,56(4):68-75,8.基金项目
中国铁路太原局集团有限公司科技研究开发计划课题(湖机技术合2023464号) （湖机技术合2023464号）
