Hybrid Method for Remaining Useful Life Prediction of Power IGBT Modules in High-Speed Trains

Accurately predicting the remaining useful life (RUL) of power insulated gate bipolar transistor (IGBT) modules is crucial for high-speed trains. Challenges under actual train operations, including significant uncertainty, multivariability, and insufficient full-lifecycle datasets of performance degradation parameters (PDPs), hinder the accurate RUL prediction. Thus, a hybrid RUL prediction approach is proposed. To address the significant uncertainty in PDP, variational mode decomposition (VMD) is utilized to segregate low-frequency trend information from the high-frequency uncertainty information, enabling distinct prediction approaches for diverse data. Besides, kernel density estimation-bidirectional long short-term memory network (KDE-BiLSTM) is proposed to precisely quantify and forecast the significant uncertainty information. Moreover, to tackle the multivariability of PDP, a Wiener-based transferable trend information modeling technique is introduced. Furthermore, to predict PDP trends with insufficient datasets, a model-based method employing regularized particle filter-RIME-least squares support vector machine (RPF-RIME-LSSVM) is proposed for trend information prediction. RIME-LSSVM solves the missing observation problem in the RPF prediction phase. Ultimately, the integration of the trend and uncertainty information yields the final RUL prediction. The proposed method was validated utilizing Infineon 6500 V/750 A IGBT modules. The maximum RUL prediction error was 9000 cycles, validating the method's effectiveness. The prediction error of at least 7000 cycles below the baseline models demonstrated the method's superiority.