Rolling　bearings　are　widely　used　in　rotating　equipment.　Detection　of　bearing　faults　is　of　great　importance　to　guarantee　safe　operation　of　mechanical　systems.　Acoustic　emission　(AE),　as　one　of　the　bearing　monitoring　technologies,　is　sensitive　to　weak　signals　and　performs　well　in　detecting　incipient　faults.　Therefore,　AE　is　widely　used　in　monitoring　the　operating　status　of　rolling　bearing.　This　paper　utilizes　Empirical　Wavelet　Transform　(EWT)　to　decompose　AE　signals　into　mono-components　adaptively　followed　by　calculation　of　the　correlated　kurtosis　(CK)　at　certain　time　intervals　of　these　components.　By　comparing　these　CK　values,　the　resonant　frequency　of　the　rolling　bearing　can　be　determined.　Then　the　fault　characteristic　frequencies　are　found　by　spectrum　envelope.　Both　simulation　signal　and　rolling　bearing　AE　signals　are　used　to　verify　the　effectiveness　of　the　proposed　method.　The　results　show　that　the　new　method　performs　well　in　identifying　bearing　fault　frequency　under　strong　background　noise.