Graph　neural　networks　(GNNs)　can　capture　interdependencies　between　data　with　the　structured　data　modeling　ability,　and　have　received　much　attention　from　industry　professionals　in　remaining　useful　life　(RUL)　prediction　tasks.　However,　the　existing　methods　assume　that　graph　nodes　and　edges　are　of　the　same　homogeneous　attributes,　which　leads　to　information　loss　and　cannot　fully　capture　the　complex　degeneration　pattern　and　topological　relationship　of　the　bearings.　To　solve　this　problem,　a　novel　heterogeneous　graph　representation-driven　multiplex　aggregation　graph　neural　network　is　proposed　for　bearing　RUL　prediction.　Different　from　the　conventional　methods　based　on　homogeneous　graphs,　we　model　the　heterogeneous　attributes　of　bearing　data　and　parameterize　the　representation　of　node　relationships　in　heterogeneous　graphs.　The　node　adjacency　is　represented　as　the　heterogeneity　belonging　to　the　designed　spatial　meta-path　and　temporal　meta-path,　respectively.　In　addition,　a　multiplex　aggregation　heterogeneous　graph　neural　network　(MAHGNN)　is　proposed　to　extract　heterogeneous　features　of　the　graph　as　well　as　temporal　dependencies　of　each　node　and　achieve　the　bearing　RUL　prediction.　In　particular,　a　novel　hierarchical　aggregation　mechanism　for　graph　heterogeneous　attributes　is　designed,　which　includes　node-level　aggregation,　path-level　aggregation　and　time-level　aggregation.　This　mechanism　can　capture　the　diverse　relationships　and　significance　of　various　types　of　nodes　and　edges　in　heterogeneous　graphs,　so　as　to　aggregate　the　feature　information　of　nodes　within　a　meta-path　and　different　meta-paths　as　well　as　extract　the　temporal　dependencies.　The　experiments　conducted　on　two　datasets　provide　evidence　for　the　superiority　of　the　proposed　method　in　comparison　to　other　state-of-the-art　RUL　prediction　methods　based　on　homogeneous　graphs.