In　the　complex　background　noise,　it　is　difficult　to　extract　the　periodic　pulse　of　the　rolling　bearing　fault　signal.　In　this　article,　a　new　modal　decomposition　method　supported　by　the　singular　value　ratio　(SVR)　spectrum　is　proposed　to　find　the　optimal　period　of　bearing　fault　data,　which　can　be　named　periodic　detection　mode　decomposition　(PDMD).　To　detect　the　intervals　of　periods　and　eliminate　the　interference　of　useless　periods,　the　initial　measurement　intervals　in　this　method　are　divided　according　to　the　theoretical　fault　periods　of　different　fault　types　of　bearings.　In　each　interval,　the　SVR　spectrum　is　used　to　detect　the　appropriate　period　and　suppress　the　influence　of　noise　on　the　recognition　process.　This　period　is　used　to　construct　the　optimal　Ramanujan　subspace　(RS).　Finally,　harmonic　spectral　kurtosis　(HSK)　is　used　to　identify　the　extracted　period　as　false　information,　interference,　or　fault.　Simulation　and　experimental　data　verify　the　effectiveness　of　the　proposed　method.　This　method　can　effectively　extract　and　screen　periodic　pulses　and　can　successfully　identify　the　outer　and　inner　ring　faults　of　bearings.