基于神经网络的IGBT模块剩余使用寿命预测模型

对IGBT模块使用寿命进行预测是评估其健康状态和可靠性的有效手段。基于IGBT老化实验测量,构建了包括饱和压降和结温的二维IGBT状态检测指标。对于归一化后的数据,提出了分段处理方法,去除了IGBT键合线断裂引起的较大指标波动。以饱和压降和结温数据为基础,提出了基于BP神经网络算法的IGBT剩余寿命预测模型。针对同样本不同通道、不同实验条件样本等情况,验证了本模型在剩余寿命预测中的准确性。

Neural network-based IGBT module Remaining useful life prediction model

Predicting the service life of IGBT modules is an effective way to assess their health and reliability. Based on the IGBT aging experiment data, This paper constructs an IGBT state detection index including a two-dimensional parameter of the saturation voltage VCE (ON) and the junction temperature Tj. For the normalized data, this paper introduces a way of segmented processing to remove the large index fluctuation caused by the break of the IGBT bond wire. Then, it performs the regression analysis on the change considering only the aging of the solder layer and find the fitted aging data curve as well as approximate function expression. To find the relationship between the parameters of IGBT module, this solution builds a prediction model of IGBT aging based on BP neural network. The accuracy of the neural network model in the prediction of remaining life is analyzed for the same sample with different channels and different experimental conditions.