An Incremental Remaining Useful Life Prediction Method Based on Wasserstein GAN and Knowledge Distillation

The precise and prompt estimation of remaining useful life (RUL) for equipment under diverse operating conditions can assist in proactive equipment maintenance and prevent failures that may lead to financial loss and casualties. This article proposes a novel task-incremental RUL prediction method based on Wasserstein GAN with gradient penalty and Knowledge Distillation (WGAN-KD) to achieve high-precision and rapid prediction. WGAN-KD develops a dual old task retention model to ensure the retention of old tasks while facilitating the acquisition of new ones. To evaluate the performance of WGAN-KD, several experiments were conducted on rolling bearings under diverse operating conditions. The experimental results demonstrated that WGAN-KD outperforms the compared incremental learning methods in accuracy under different operating conditions. Furthermore, it maintains high-precision prediction while enhancing training efficiency compared to batch learning methods.