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.　©　2024　Elsevier　Ltd