基于CNN-Attention-LSTM的IGBT键合线失效状态评估OA

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.