A　novel　fault　diagnosis　scheme　was　developed　to　address　the　difficulty　of　feature　extraction　for　planetary　gearboxes　using　refined　composite　multiscale　fluctuation　dispersion　entropy　(RCMFDE)　and　supervised　manifold　mapping.　The　RCMFDE　was　first　utilized　in　this　scheme　to　fully　mine　fault　features　from　planetary　gearbox　signals　under　multiple　scales.　Subsequently,　as　a　supervised　manifold　mapping　method,　supervised　isometric　mapping　(S-Iso)　was　applied　to　decrease　the　dimensions　of　the　original　features　and　remove　redundant　information.　Lastly,　the　marine　predator　algorithm-based　support　vector　machine　(MPA-SVM)　classifier　was　employed　to　achieve　intelligent　fault　diagnosis　of　planetary　gearboxes.　The　suggested　RCMFDE　combines　the　composite　coarse-grained　construction　and　refined　computing　technology,　overcoming　unstable　and　invalid　entropy　in　the　traditional　multiscale　fluctuation　dispersion　entropy.　Simulation　experiments　and　fault　diagnosis　experiments　from　a　real　planetary　gearbox　drive　system　show　that　the　complexity　measure　capability　and　feature　extraction　effectiveness　of　the　proposed　RCMFDE　outperform　the　multiscale　fluctuation　dispersion　entropy　(MFDE)　and　multi-scale　permutation　entropy　(MPE).　The　S-Iso’s　visualization　results　and　dimensionality　reduction　performance　are　better　than　principal　components　analysis　(PCA),　linear　discriminant　analysis　(LDA),　and　isometric　mapping　(Isomap).　Moreover,　the　suggested　fault　diagnosis　scheme　has　an　accuracy　rate　of　100%　in　identifying　bearing　and　gear　defects　in　planetary　gearboxes.　©　2023　by　the　authors.