A Multi-source Data-driven Approach to IGBT Remaining Useful Life Prediction

IGBT (insulated gate bipolar transistor) undergo aging failures due to environmental effects during operation. It is of theoretical importance and engineering value to study how to predict the remaining useful life (RUL) of IGBT using multi-source data. In this paper, we propose a multi-source data-driven IGBT RUL prediction method, which extracts the time-domain features of the degradation process by utilizing the operating characteristics of IGBT and employs a bi-directional gated recurrent unit neural network to improve the prediction performance. First, the corresponding current-voltage signals are extracted from the aging data according to the IGBT operating characteristics, and the time-domain features are calculated; second, the random forest algorithm is used to rank the importance of the features, and the important features are screened out to reduce the redundancy of the features; finally, the RUL prediction model based on the BiGRU network is established, and the validity of the method is verified by the actual IGBT degradation feature data. The results show that the proposed method combines a variety of aging data, fully considers multiple factors in the working condition, and has higher RUL prediction accuracy and stability.