The　fast　kurtogram　(FK),　as　a　fast　and　effective　method　for　fault　diagnosis,　is　well　accepted　by　many　experts　and　scholars.　However,　the　FK　can　only　estimate　the　bandwidth　and　central　frequency　which　come　from　resonance　modulation　of　the　signal.　Sometimes　useful　information　(containing　faults)　may　be　lost　due　to　the　inaccuracy　of　the　estimated　center　frequency　or　bandwidth.　In　this　paper,　a　novel　method　named　empirical　scanning　spectrum　kurtosis　(ESSK),　based　on　empirical　wavelet　transform　(EWT),　is　proposed.　Constructed　by　the　principle　of　EWT,　a　set　of　filters　with　varying　bandwidth　scan　and　filter　the　whole　frequency　domain　from　low　to　high　and　a　series　of　empirical　modal　components　are　obtained.　Then,　the　spectral　kurtosis　(SK)　of　these　components　is　calculated.　The　center　frequency　and　bandwidth　corresponding　to　the　component　which　has　the　maximum　SK　are　selected　as　the　optimal　center　frequency　and　bandwidth.　This　method　can　adaptively　and　accurately　find　the　frequency　band　containing　rich　fault　feature　information,　and　extract　the　corresponding　component.　Multiple　simulation　signals　and　experimental　signals　are　used　to　verify　the　effectiveness　of　the　proposed　method.　The　results　show　that　the　method　can　maximally　extract　the　components　which　contain　the　periodic　pulse　information　and　accurately　diagnose　the　faults　of　the　rolling　bearing.　In　addition,　comparisons　with　three　popular　signal　processing　methods,　including　the　sparsogram,　fir-based　FK　and　short-time　Fourier　transform　(STFT)based　FK　are　conducted　to　highlight　the　superiority　of　the　proposed　method.