Rolling　bearings　are　essential　supporting　components　for　most　rotating　machinery　and　are　commonly　placed　at　great　risk　of　sudden　failure.　Accurate　prediction　of　the　remaining　service　life　of　rolling　bearings　is　essential　for　ensuring　reliable　operation　and　establishing　an　effective　maintenance　strategy.　Focusing　on　the　extreme　learning　machine　(ELM)　methodology,　an　innovative　predictive　model　with　error　feedback　neuron　integration　is　established　to　eliminate　the　deficiency　in　model　generalization　capability.　To　further　improve　the　predictive　accuracy,　an　improved　bat　algorithm　(IBA)　is　introduced　into　the　FELM　model,　in　which　the　Levy　flight　and　frequency　influence　factor　are　embedded　into　the　traditional　BA　algorithm　to　enhance　the　parameter　searching　ability.　Inverse　hyperbolic　function-based　statistical　indicators　are　proposed　and　verified　by　comparing　with　the　classical　RMS　curve　of　full-life　data,　whose　cosine　similarity　and　correlation　coefficient　both　exceed　0.95.　Two　sets　of　accelerated　life　experiments　were　selected　to　validate　the　effectiveness　of　the　proposed　IBA-FELM　model.　The　results　show　that　the　integrated　model　can　obtain　high　prediction　accuracy　and　satisfactorily　fit　the　real-life　data.　The　maximal　prediction　error　can　be　reduced　from　1.57　to　0.0401　for　experimental　Case　1,　and　from　0.7375　to　0.1492　for　Case　2.　Compared　with　the　other　machine　learning　models,　such　as　SVR,　CNN,　and　LSTM　networks,　the　IBA-FELM　model　also　presents　stronger　optimization　ability,　higher　generalization　performance,　and　operation　stability.