Condition-based　maintenance　(CBM),　as　a　key　component　of　asset　health　management,　is　crucial　to　enhance　the　operational　safety　and　availability　of　diverse　mechatronic　systems,　such　as　railway　vehicles,　wind　power　equipment,　nuclear　devices,　etc.　A　common　phenomenon　observed　in　CBM　is　the　existence　of　dispersibility　regarding　degradation-induced　failure　threshold,　which　affects　the　precision　of　maintenance　decisions.　This　article　addresses　such　challenges　by　scheduling　a　prognosis-centered　intelligent　CBM　policy,　which　harnesses　dynamic　lifetime　information　to　support　both　scheduled　and　opportunistic　maintenance　decision-making.　The　degradation　is　characterized　by　a　generalized-form　stochastic　process,　and　the　lifetime　distribution　is　assessed　through　the　fusion　of　multiple　uncertainties.　A　dynamic　reliability　criterion　is　set　to　determine　whether　and　when　to　postpone　maintenance,　whose　interval　is　controlled　by　the　remaining　lifetime　as　well　as　an　optimizable　safety　coefficient.　The　postponement　interval,　in　turn,　enables　the　planning　of　opportunistic　maintenance　to　mitigate　system　downtime.　The　operational　cost　rate　is　minimized　through　the　joint　optimization　of　the　inspection　interval,　conditional　reliability　threshold,　and　safety　coefficient.　The　superiorities　of　the　proposed　policy　over　some　conventional/heuristic　maintenance　policies　are　demonstrated　by　a　case　study　on　filed　maintenance　planning　of　high-speed　train　bearing.