The　demand　of　high　similarity　between　the　matching　basis　function　and　expected　feature　is　required　by　the　inner　product　transform　principle.　However,　without　precise　information　of　the　potential　fault　features,　the　deterministic　or　statistical　characteristics　of　the　investigated　features　are　beneficial　to　the　selection　and　construction　of　proper　matching　bases.　According　to　the　intrinsic　periodic　sparsity　phenomena　of　repetitive　impulsive　fault　features,　a　periodic　sparsity　based　oriented　super-wavelet　transform　is　proposed.　The　super-wavelet　transform　is　constructed　based　on　the　tunable　Q-factor　wavelet　transform　(TQWT)　and　presented　as　an　improvement　to　the　conventional　idea　of　unique　and　fixed　basis.　Within　the　procedure,　the　super-wavelet　dictionary　functions　are　applied　to　decompose　signals;　an　indicator　estimating　the　periodic　sparsity　feature　energy　ratio　(PSFER)　is　adopted　to　guide　the　selection　of　TQWT＇s　parameters;　the　selected　optimal　super-wavelet　basis　is　utilized　to　reveal　the　hidden　fault　features　in　the　signal.　The　proposed　technique　is　applied　to　acquire　the　incipient　fault　features　of　a　motor　bearing　on　a　piece　of　wind　power　generation　equipment,　and　the　extracted　features　proved　to　be　associated　with　an　actual　bearing　fault.　©　2016　Journal　of　Mechanical　Engineering.