Remaining useful life prediction with spatio-temporal graph transform and weakly supervised adversarial network: An application in power components

Remaining useful life (RUL) prediction is crucial in improving the reliability of insulated gate bipolar transistors (IGBTs) and has attracted widespread attention all over the world. Owing to the heterogeneity and complex internal connectivity of the IGBTs, accurate RUL prediction by traditional deep learning (DL) methods remains challenging. Herein, a novel prognostic framework is proposed for their RUL prediction. In brief, the non-smooth spike voltage of IGBTs is transformed into a graph matrix that contains rich correlation information. Then, spatio-temporal fusion graph network (STFGN) is proposed to realize RUL prediction. It contains spatial-scale and temporal-scale extractors, where the former one based on GraphSage can obtain unique IGBTs data structure information, while the latter based on bidirectional long short-term memory (BiLSTM) captures the time-series degradation features of IGBTs. Moreover, the weakly supervised adversarial learning strategy is considered to realize cross-domain prediction using scarce labeled data, which enhance the positive transfer. Four transfer tasks under variable working conditions are considered to prove the proposed method. The experimental results demonstrate that the average SF and RMSE metrics of the proposed method are 0.429 and 0.108, which is superior to other related methods.