Resonance　demodulation　is　a　commonly　used　method　to　obtain　fault　information,　whose　main　challenge　is　to　seek　a　suitable　frequency　band　for　demodulation.　Autogram　is　a　recently　proposed　method　for　optimal　demodulation　frequency　band　(ODFB)　selection,　in　which　the　frequency　domain　is　divided　by　a　binary　tree　structure.　However,　the　frequency　band　information　obtained　by　Autogram　is　easily　missed.　To　solve　this　issue,　a　1/3　binary　tree　structure　is　employed　to　segment　the　frequency　domain　and　improve　the　segmentation　accuracy　of　Autogram.　Despite　this,　the　frequency　bands　with　different　position　information　cannot　be　fully　obtained　through　the　above-mentioned　structure　yet.　In　this　article,　the　traverse　symplectic　correlation-gram　(TSCgram)　is　proposed　to　overcome　the　shortcomings　existing　in　Autogram　and　based　on　that,　a　novel　fault　diagnosis　method　for　rolling　bearing　is　proposed.　First,　the　frequency　domain　of　the　raw　vibration　signal　of　rolling　bearing　is　accurately　divided　through　the　traversal　segmentation　structure.　Then,　the　concept　of　symplectic　correlation　is　introduced　to　reduce　the　interference　of　noise,　as　well　as　other　unrelated　components　on　the　traditional　kurtosis,　thus　further　improving　the　accuracy　of　ODFB　detection.　Finally,　the　band　position　corresponding　to　the　maximum　symplectic　correlation　kurtosis　(SCK)　value　is　chosen　as　the　ODFB　for　fault　diagnostics　of　rolling　bearing.　Also,　the　proposed　method　is　applied　to　the　simulated　and　measured　data　analysis　by　comparing　it　with　fast　kurtogram　and　Autogram　methods.　The　comparison　and　analysis　results　indicate　that　the　fault　diagnostic　effect　of　the　proposed　TSCgram　method　is　better　than　the　comparing　methods.