Digital Twin-Driven Graph Domain Adaptation Neural Network for Remaining Useful Life Prediction of Rolling Bearing

Remaining useful life (RUL) prediction is significant for the healthy operation of machinery. In order to accurately identify the bearing degeneration states, it is necessary to collect massive full lifecycle data. However, the bearing lifecycle data is insufficient for effectively training a RUL prediction model in engineering practice. In this paper, a digital twin-driven graph domain adaptation method is proposed. First, a full lifecycle dynamic twin model of bearings is constructed to generate abundant twin data, in which the surface morphology evolution and roller relative slip at different stages are simulated to generate vibration responses. Second, a novel multi-layered cross-domain gated graph convolutional network (MGGCN) is developed, in which a new graph domain adaptation model is designed to solve the problem that traditional domain adaptation methods are not effective in processing the non-Euclidean data. The spatial and temporal features are extracted by multiple nonlinear transformations and previous time-step hidden state incorporation, respectively. In addition, a graph Laplacian regularized maximum mean discrepancy (GLMMD) is designed and applied in the training of model to enhance the capability of discerning graph domain differences. The experimental results confirm that the proposed method can achieve effective performance even in scenarios with limited actual data.