Singular　spectrum　decomposition　(SSD)　is　a　new　adaptive　signal　processing　method　for　nonlinear　and　non-stationary　signals.　By　constructing　a　trajectory　matrix　and　adaptively　selecting　the　embedding　dimensions,　the　method　divides　non-stationary　signals　into　several　single-component　signals　successively　from　high　frequency　to　low　frequency.　However,　in　the　process　of　component　reconstruction,　bandwidth　estimation　and　determining　sizable　trends　by　building　a　Gaussian　function　superposition　spectral　model　are　extremely　complicated.　Moreover,　the　parameter　setting　requires　too　much　manual　intervention　and　lacks　theoretical　support.　Hence,　aimed　at　nonlinear　and　non-stationary　vibration　signals　of　rolling　bearings,　a　novel　method　of　fault　feature　extraction　based　on　the　order　statistic　filter　(OSF)　for　fast　SSD　(FSSD)　is　proposed.　The　FSSD　method　adopts　the　envelope　method　of　OSFs　to　divide　the　spectrum　and　determine　the　sizable　trend　to　improve　the　process.　The　proposed　method　is　applied　to　bearing　fault　diagnosis.　The　analysis　results　of　simulation　signals　and　bearing　experimental　signals　show　that　the　new　method　can　decompose　signals　quickly,　effectively　and　accurately,　and　the　mode　mixing　and　time-consuming　problems　are　refined.