Empirical　wavelet　transform　is　good　at　distinguishing　components　containing　different　frequency　information　in　complex　signals.　Due　to　the　higher　complexity　of　the　Fourier　spectrum,　the　original　method　would　generate　a　large　number　of　boundaries,　more　invalid　components.　The　division　method　without　considering　the　fluctuation　characteristics　will　affect　the　results.　In　this　paper,　adaptive　and　concise　empirical　wavelet　transform　(ACEWT)　is　proposed.　The　power　spectral　density　of　the　signal　is　calculated　and　used　to　segment　the　spectrum,　which　can　reduce　the　number　of　extreme　points　and　the　dependence　on　them　in　the　original　method.　Weighted　unbiased　autocorrelation　(WAC)　that　can　filter　bearing　fault　information　is　proposed.　After　combining　ACEWT　and　WAC,　a　tower　boundaries　distribution　diagram　(W-Autogram)　which　can　be　used　to　extract　specific　information　is　proposed.　Simulation　signals　and　experimental　results　verify　that　the　proposed　method　can　be　applied　to　the　fault　diagnosis　of　rolling　bearings　in　rotating　machinery.