Ensemble　empirical　mode　decomposition　(EEMD)　is　a　good　method　for　suppressing　the　mode　mixing　in　empirical　mode　decomposition　(EMD),　but　its　decomposition　effect　depends　on　two　important　parameters,　which　are　the　amplitude　of　added　noise　and　the　number　of　ensemble　for　EEMD.　On　the　present　time,　these　two　important　parameters　mainly　depend　on　experience　and　lack　of　reliability.　In　order　to　solve　above　problems,　an　improved　algorithm　named　adaptive　ensemble　empirical　mode　decomposition　(AEEMD)　is　proposed.　Firstly,　the　amplitude　standard　deviation　of　original　signal　is　computed;　secondly,　the　original　signal　is　decomposed　by　high-pass　filters,　and　then　the　amplitude　standard　deviation　of　high　frequency　components　is　computed.　Thus　the　amplitude　standard　deviation　coefficient　of　added　noise　can　be　determined　by　the　amplitude　standard　deviation　of　original　signal　and　its　high　frequency　components.　Thirdly,　the　number　of　ensemble　can　be　determined　by　the　expectation　AEEMD　decomposition　error　and　the　amplitude　standard　deviation　coefficient　of　added　noise.　Lastly,　in　order　to　decrease　the　mode　mixing　further　and　enhance　the　orthogonality　of　adjacent　intrinsic　mode　function,　an　AEEMD　post-processing　method　using　EMD　is　proposed.　The　results　of　the　simulation　and　experiments　indicate　that　the　proposed　AEEMD　algorithm　can　overcome　mode　mixing,　and　is　successfully　applied　in　the　fault　diagnosis　of　rotary　machine,　the　result　demonstrates　that　mode　mixing　can　be　controlled　effectively,　and　then　is　compared　with　basic　EMD　algorithm.　The　results　show　that　AEEMD　is　more　precise,　and　it　can　exactly　recognize　rotary　machine　faults.