Rolling　bearings　are　the　key　components　of　rotating　machinery.　Thus,　the　prediction　of　remaining　useful　life　(RUL)　is　vital　in　condition-based　maintenance　(CBM).　This　paper　proposes　a　new　method　for　RUL　prediction　of　bearings　based　on　time-varying　Kalman　filter,　which　can　automatically　match　different　degradation　stages　of　bearings　and　effectively　realize　the　prediction　of　RUL.　The　evolution　of　monitoring　data　in　normal　and　slow　degradation　stages　is　a　linear　trend,　and　the　evolution　in　accelerated　degradation　stage　is　nonlinear.　Therefore,　Kalman　filter　models　based　on　linear　and　quadratic　functions　are　established.　Meanwhile,　a　sliding　window　relative　error　is　constructed　to　adaptively　judge　the　bearing　degradation　stages.　It　can　automatically　switch　filter　models　to　process　monitoring　data　at　different　stages.　Then,　the　RUL　can　be　predicted　effectively.　Two　groups　of　bearing　run-to-failure　data　sets　are　utilized　to　demonstrate　the　feasibility　and　validity　of　the　proposed　method.