A　new　Switching　Unscented　Kalman　Filter　(SUKF)　algorithm　is　proposed.　The　corresponding　state-space　models　for　each　kind　of　bearing　operation　state　are　established,　and　the　UKF　algorithm　is　incorporated　into　the　Bayesian　estimation　method　to　calculate　the　probability　of　each　state　at　every　time　and　determine　the　most　probable　state.　The　prediction　of　Remaining　Useful　Life　(RUL)　can　be　carried　out　once　the　accelerated　degradation　stage　is　detected.　In　order　to　make　the　filtering　results　of　Condition　Monitoring　(CM)　data　smoother　and　avoid　misjudgment　of　status　when　the　degradation　speed　is　negative,　the　measurement　error　parameter　is　selected　as　the　standard　deviation　of　CM　data　in　the　degradation　stage.　The　proposed　method　is　applied　into　the　bearing　CM　data　from　Intelligent　System　Maintenance　Center　of　University　of　Cincinnati.　Besides,　it　is　also　compared　with　the　traditional　Switching　Kalman　Filter　(SKF)　algorithm.　The　results　show　the　effectiveness　of　the　proposed　method.　(C)　2018　Elsevier　Ltd.　All　rights　reserved.