The　remaining　useful　life　(RUL)　prediction　of　rolling　element　bearing　has　attracted　substantial　attention　due　to　its　importance　in　improving　reliability　and　safety　of　machines.　Particle　filter　(PF)　algorithm　is　widely　utilized　to　track　the　degradation　process　of　mechanical　equipment　in　RUL　prediction.　In　engineering　applications,　due　to　the　poor　consistency　of　bearings,　the　classical　PF　model　with　a　certain　model　finds　it　difficult　to　deal　with　bearings　with　different　degradation　trends,　resulting　in　poor　generalization　ability.　Therefore,　a　time-varying　PF　(TVPF)-based　comprehensive　RUL　(CRUL)　prediction　model　is　developed　in　this　article.　In　the　model,　a　TVPF　algorithm　is　constructed　by　an　adaptive　selection　rule　and　sliding　window,　it　has　the　capability　to　select　the　optimal　state　model　with　a　sliding　window,　according　to　the　characteristics　of　the　data,　and　track　the　degradation　state　of　bearings　with　different　degradation　trends;　meanwhile,　a　global/local　information　fusion　(GLIF)　technique　is　proposed　for　comprehensively　considering　the　overall　information　and　the　latest　degraded　state　of　the　rolling　bearings.　The　effectiveness　of　the　proposed　method　is　verified　by　two　datasets　with　different　degradation　trends,　respectively.　The　comparative　study　indicates　that　the　proposed　TVPF　algorithm　outperforms　the　other　state-of-art　methods　in　RUL　prediction　and　system　prognosis　with　respect　to　better　accuracy　and　robustness.