It　is　very　important　to　select　the　optimal　demodulation　frequency　band　(ODFB)　of　rolling　bearing　vibration　signals　for　the　most　valuable　fault　information　extraction　and　diagnosis.　Fast　kurtogram　(FK)　is　an　effective　and　most　commonly　used　ODFB　selection　approach　for　bearing　fault　diagnosis,　which　generally　is　founded　on　the　filter　bank　structure　and　short-time　Fourier　transform.　Though　the　FK　method　can　effectively　detect　the　shock　characteristics　of　frequency　band　signals,　other　useful　characteristics　related　with　failure　of　vibration　signal　will　be　ignored.　In　this　paper,　a　novel　ODFB　selection　method　called　traversal　index　enhanced-gram　(TIEgram)　is　proposed　for　rolling　bearing　vibration　signals.　In　the　proposed　TIEgram　method,　first　of　all,　the　traversal　segmentation　model　is　utilized　to　transfer　the　original　signal　into　frequency　domain　for　enhancing　overall　segmentation　performance　of　traditional　binary　trees　and　1/3　binary　trees　structure　segmentation　models.　Then,　a　new　weighted　fusion　indicator　based　on　the　kurtosis,　correlation　coefficient　and　spectral　negative　entropy　is　designed　to　select　ODFB　from　the　segmented　results　of　traversal　segmentation　model,　which　can　effectively　solve　the　problem　that　different　vibration　signal　characteristics　cannot　be　fully　detected　by　a　single　indicator.　After　that,　an　enhanced　adaptive　multi-scale　weighted　morphological　filtering-based　envelope　spectrum　is　employed　to　demodulate　the　obtained　frequency　band　for　a　much　more　accurate　diagnosis　effect　of　rolling　bearing.　Finally,　the　simulated　and　measured　signals　of　rolling　bearing　under　stationary　and　non-stationary　operating　conditions　are　respectively　used　to　verify　the　feasibility　and　effectiveness　of　the　proposed　method　with　comparison　of　the　existing　FK,　Autogram　and　infogram　methods.　The　comparison　analysis　results　show　that　TIEgram　method　can　accurately　identify　the　most　useful　fault　information　and　shows　better　performance　than　existing　methods.　©　2022　Elsevier　Ltd