A　typical　vibration　signal　of　fault　bearing　is　composed　of　periodic　repetitive　transient　impulses,　multiple　vibration　disturbance　and　background　noise.　Variational　mode　decomposition　(VMD)　represents　a　potential　tool　for　analyzing　such　signals.　However,　the　reasonable　selection　of　VMD　algorithm　parameters　hinders　its　application　in　mechanical　signal　processing　to　a　certain　extent.　According　to　the　specific　characteristics　of　rolling　bearing　fault　signal,　the　composite　dimensionless　index　is　constructed　as　the　objective　function　to　ensure　the　optimal　decomposition　of　VMD.　To　further　enhance　the　fault　characteristics,　the　tunable　Q-factor　wavelet　transform　(TQWT)　along　with　sparse　code　shrinkage　is　proposed　to　denoise　the　modal　components　containing　periodic　impulses,　which　further　highlights　the　impulses　and　improves　the　sparseness　of　fault　signal.　Simulation　and　experimental　signal　analysis　verify　the　effectiveness　and　reliability　of　this　method.　The　results　show　that　the　use　of　optimized　VMD　and　TQWT　based　sparse　code　shrinkage　dramatically　sharpens　the　impulses　from　the　mixed　signal　with　noise　interference　and　increases　the　sparseness　to　a　level.