Multi-scale　permutation　entropy　(MPE)　has　been　proven　to　be　an　effective　nonlinear　dynamic　analysis　tool　for　complexity　and　irregularity　evaluation　of　rolling　bearing.　Nevertheless,　MPE　still　has　some　issues　that　need　to　be　addressed.　First,　the　coarse　grained　process　used　in　MPE　will　shorten　the　length　of　time　series　and　result　in　mode　information　loss,　especially　for　short　time　series.　Second,　different　patterns　of　a　symbol　cannot　be　distinguished　by　permutation　entropy　and　MPE.　Inspired　by　the　thought　of　composite　coarse　graining　and　weighted　permutation　entropy,　the　composite　multi-scale　weighted　permutation　entropy　(CMWPE)　methodology　is　proposed　in　this　paper.　Compared　with　MPE,　CMWPE　preserves　much　more　useful　information　by　adding　the　weighted　factor　and　using　composite　coarse　graining　to　optimize　the　process　of　coarse-gained　time　series,　where　multiple　time　series　information　is　considered　for　the　same　scale　factor.　The　simulation　synthetic　signals　are　used　to　demonstrate　the　effectiveness　of　CMWPE　and　the　results　show　that　CMWPE　has　less　dependence　on　data　length　and　the　estimated　entropy　values　are　much　more　stable　than　the　other　existing　methods.　Based　on　CMWPE,　a　new　intelligent　fault　diagnosis　scheme　for　rolling　bearing　is　proposed　with　combination　of　extreme　learning　machine.　Finally,　the　proposed　fault　diagnosis　method　is　applied　to　two　diagnostic　cases　of　rolling　bearing　and　the　results　verified　the　effectiveness　and　superiority　of　the　proposed　approach　to　MPE　and　MWPE.　(C)　2019　Elsevier　Ltd.　All　rights　reserved.