As　essential　but　easily　damaged　parts　of　rotating　machinery,　rolling　bearings　have　been　deeply　researched　and　widely　used　in　mechanical　processes.　The　real-time　detection　of　bearing　state　and　simple,　rapid,　and　accurate　diagnosis　of　bearing　fault　are　indispensable　to　the　industrial　system.　The　bearing＇s　inner　ring　and　outer　ring　vibration　acceleration　can　be　measured　by　high-precision　sensors,　and　the　running　state　of　the　bearing　can　be　effectively　extracted.　The　empirical　wavelet　transform　(EWT)　can　adaptively　decompose　the　vibration　acceleration　signal　into　a　series　of　empirical　modes.　However,　this　method　not　only　runs　slowly,　but　also　causes　inexplicable　empirical　modes　due　to　the　unreasonable　boundaries　of　the　frequency　domain　division.　In　this　paper,　a　new　method　is　proposed　to　improve　the　empirical　wavelet　transform　by　dividing　the　boundaries　from　the　spectrum,　named　the　fast　empirical　wavelet　transform　(FEWT).　The　proposed　method　chooses　different　points　in　the　Fourier　transform　of　the　spectrum　(key　function)　to　reconstruct　the　trend　component　of　the　spectrum.　The　minimum　points　in　the　trend　component　divide　the　spectrum　into　a　series　of　bands.　A　more　reasonable　set　of　boundaries　can　be　found　by　choosing　appropriate　trend　components　to　obtain　effective　empirical　modes.　The　simulation　results　show　that　the　proposed　method　is　effective　and　that　the　acquired　empirical　mode　is　more　reasonable　than　the　EWT　method.　Combining　kurtosis　with　fault　feature　extraction　of　inner　and　outer　rings　of　bearings,　the　method　is　successfully　applied　to　the　fault　diagnosis　of　rolling　bearings.