As　a　pivotal　technology　of　Prognostic　and　Health　Management,　the　remaining　useful　life　(RUL)　prediction　techniques　significantly　contribute　to　predictive　maintenance　and　ensure　the　safe　operation　of　mechanical　equipment.　Nevertheless,　the　current　similarity-based　prediction　(SBP)　methods　face　challenges　in　effectively　utilizing　the　degradation　information　encapsulated　within　a　limited　number　of　degradation　samples.　Therefore,　an　integrated　dual-scale　similarity-based　prediction　(IDS-SBP)　method　is　proposed　bearing　RUL　prediction,　which　can　fully　mine　the　degradation　information　of　the　samples　from　two　distinct　time　scales.　Specifically,　a　whole　lifecycle　dynamic　model　is　constructed　to　describe　the　various　long-term　degradation　processes　for　bearings,　which　enriches　the　variety　of　the　performance　degradation　samples.　Subsequently,　the　dual-scale　matching　strategy　is　designed　to　extract　the　degradation　information　from　two　different　time　scales.　Meanwhile,　the　designed　lifetime　calibration　technique　can　calibrate　the　lifetime　of　samples　by　considering　the　degradation　rate.　Finally,　the　uncertainty　analysis　is　conducted　to　integrate　the　prediction　results　at　different　time　scales,　thereby　achieving　the　comprehensive　evaluation　of　test　bearings.　Several　sets　of　experimental　data　are　applied　to　verify　the　prediction　performance　of　the　proposed　method,　and　prediction　results　confirm　that　the　proposed　method　achieves　great　prediction　accuracy　and　superior　generalization　ability.