In　industrial　production,　it　is　necessary　to　detect　the　running　state　of　rolling　bearings　and　diagnose　their　faults.　When　rolling　bearing　is　damaged,　the　vibration　signals　collected　often　show　the　characteristics　of　non-stationary　and　modulation,　and　will　inevitably　be　disturbed　by　strong　noise,　so　it　is　very　difficult　to　identify　the　fault　features.　How　to　effectively　extract　the　components　carrying　fault　feature　information　from　complex　non-stationary　and　modulated　signals　is　the　key　of　diagnosing　bearing　fault.　Frequency　slice　wavelet　transform　(FSWT)　uses　frequency　slice　function　based　on　the　advantages　of　short-time　Fourier　transform　(STFT)　and　wavelet　transform　(WT),　which　makes　the　traditional　Fourier　transform　realize　time-frequency　analysis　function.　The　traditional　fault　diagnosis　method　based　on　FSWT　determines　the　most　suitable　center　frequency　and　the　faulty　bandwidth　by　comparing　the　results　of　different　frequency　band　processing,　and　realizes　the　reconstruction　and　description　of　arbitrary　frequency　band　and　local　characteristics　of　the　signal.　However,　this　method　is　inefficient,　non-adaptive　and　can　not　guarantee　that　the　frequency　band　screened　manually　contains　the　required　fault　information.　Aiming　at　the　problem　that　traditional　methods　rely　on　manual　operation　and　have　no　self-adaptability,　an　adaptive　frequency　slice　wavelet　transform　(AFSWT)　is　proposed　in　this　paper.　Firstly,　the　signal　spectrum　is　segmented　continuously;　spectrum　segmentation　covers　the　whole　frequency　band　and　avoids　the　process　of　manual　selection　of　spectrum　boundary.　The　method　of　equalization　can　improve　the　computational　efficiency.　Secondly,　the　spectral　negative　entropy　is　introduced　as　the　evaluation　basis　to　calculate　the　complexity　of　the　signal　in　each　frequency　band　in　order　to　screen　the　cyclostationary　information　which　may　contain　periodic　shocks.　Finally,　the　frequency　band　with　the　largest　spectral　negative　entropy　is　selected　and　defined　as　the　faulty　center　frequency　and　bandwidth.　The　signal　components　in　the　band　are　reconstructed　and　analyzed　by　envelope　demodulation　to　realize　fault　diagnosis.　The　analysis　results　of　a　simulation　signal　show　that　the　AFSWT　method　identifies　the　center　frequency　of　5　000　Hz　and　the　bandwidth　of　909　Hz,　which　is　very　close　to　the　ideal　result.　Compared　with　fast　spectral　kurtosis,　AFSWT　has　better　applicability　when　the　central　frequency　of　signal　is　located　in　Fs/4,　Fs/8　and　Fs/16(Fs　is　the　sampling　frequency).　Through　the　test　of　rolling　bearing　test-bench,　the　vibration　signals　of　rolling　bearing　outer　ring　fault　are　collected　and　analyzed.　After　AFSWT　analysis,　the　characteristic　frequency　and　its　2-6　times　frequency　components　can　be　clearly　found　in　the　envelope　spectrum　of　the　results.　On　the　other　hand,　AFSWT　takes　14.7　seconds　to　process　test　signals.　The　traditional　FSWT　needs　repeated　drawing　of　time-frequency　distribution　map,　determination　of　central　frequency　band　and　selection　of　observation　frequency,　it　often　takes　5-10　minutes　to　determine　the　faulty　center　frequency　and　bandwidth.　The　above　analysis　shows　that　AFSWT　can　improve　the　calculation　efficiency　and　screening　accuracy　by　uniformly　dividing　the　spectrum　of　the　signal　and　screening　the　signal　components　according　to　the　negative　entropy　of　the　spectrum.　It　is　suitable　for　fault　diagnosis　of　rolling　bearings.　©　2019,　Editorial　Department　of　the　Transactions　of　the　Chinese　Society　of　Agricultural　Engineering.　All　right　reserved.