To　realize　bearing　life　prediction　based　on　vibration　data,　a　health　index　set　containing　time,　frequency　statistical　indices　and　inverse　trigonometric　ones　was　constructed　in　this　paper.　By　comparing　the　cosine　similarity　and　correlation　coefficient　with　the　root　mean　square　index,　the　index　set　was　screened　and　reduced.　For　solving　the　problem　of　low　accuracy　of　basic　extreme　learning　machine　(ELM)　model　caused　by　the　random　configuration　of　parameters,　a　meta-heuristic　algorithm　represented　by　bat　algorithm　(BA)　was　introduced.　As　the　traditional　BA　was　prone　to　fall　into　local　optimum,　an　improved　strategy　of　BA　was　established,　which　used　levy　flight　strategy　to　improve　the　search　ability　of　BA　and　enhanced　the　diversity　of　the　population　through　competitive　learning,　thereby　improving　the　search　efficiency　while　ensuring　search　accuracy.　The　contribution　of　the　BA　improved　strategy　to　the　life　prediction　model　was　verified　using　the　full　life　cycle　test　data　of　a　rolling　bearing.　Results　show　that　no　matter　what　working　conditions　the　test　data　set　is　in,　the　above　prediction　framework　can　obtain　a　relatively　high　prediction　accuracy,　not　only　keeping　a　high　consistency　with　the　true　life　curve　but　also　showing　stronger　generalization　performance　and　stability　than　the　RBF　and　CS-ELM　predictive　models.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.