An Improved Wiener-Based Remaining Useful Life Prediction Approach With Reduced Uncertainty for High-Power IGBT Modules

Recently, Wiener-based remaining useful life (RUL) prediction methods are widely employed to ensure device reliability. However, for high-power insulated gate bipolar transistor (IGBT) modules whose performance degradation parameters (PDPs) contain substantial noise components, existing methods introduce considerable uncertainty in RUL predictions, significantly increasing maintenance costs. Thus, a Wiener-based RUL prediction method integrated with a comprehensive uncertainty reduction strategy is proposed. Under the existing Wiener-based RUL prediction framework, the drift coefficient is modeled by replacing the divergent random walk process in conventional methods with the convergent Ornstein–Uhlenbeck (OU) process, which describes the time-varying degradation rate of the device more precisely and diminishes the RUL uncertainty. Accordingly, a dedicated parameter estimation process for the Wiener degradation model incorporating the OU process is developed. During particle filter (PF)-based prediction of the PDP trajectory, a model augmentation technique is introduced. By employing the long short-term memory (LSTM) network to extend the PDP dataset to the full life cycle, the PF maintains continuous operation throughout the lifespan of the device. This approach prevents the accumulation of variance in PDP predictions and further reduces RUL uncertainty. Ultimately, the efficacy and superiority of the proposed method were validated using the simulation dataset and the degradation dataset from the Infineon 6500 V/750 A IGBT module.