Condition　monitoring　and　fault　diagnosis　of　wind　turbines　is　an　attractive　yet　challenging　task.　This　paper　presents　a　novel　data-driven　fault　diagnosis　scheme　for　wind　turbines,　using　refined　time-shift　multiscale　fluctuation-based　dispersion　entropy　(RTSMFDE)　and　cosine　pairwise-constrained　supervised　manifold　mapping　(CPCSMM).　The　developed　RTSMFDE　can　improve　multiscale　dispersion　entropy　in　complexity　measurement　of　time　series,　by　employing　the　refined　time-shift　analysis　approach.　Moreover,　the　proposed　CPCSMM　combines　cosine　distance　metric,　supervised　theory　and　sparse　global　manifold　structure,　to　obtain　the　most　important　features,　resulting　in　a　low　dimensional　representation.　In　this　fault　diagnosis　scheme,　a　high-dimensional　fault　feature　set　is　first　constructed　using　RTSMFDE.　Then,　sensitive　features　are　extracted　using　CPCSMM.　Finally,　the　low-dimensional　feature　set　is　fed　to　a　beetle　antennae　search-based　support　vector　machine　for　fault　identification.　Simulation　experiments　and　wind　turbine　fault　diagnosis　experiments　were　used,　to　show　that　the　proposed　RTSMFDE　can　comprehensively　mine　the　wind　turbine　features　and　outperforms　the　existing　state-of-the-art　entropy　methods.　The　visualization　results　and　classification　effects　of　the　proposed　CPCSMM　approach　are　better　than　existing　unsupervised　and　supervised　dimensionality　reduction　methods.　The　proposed　data-driven　fault　diagnosis　method　can　effectively　and　accurately　identify　multi-faults　and　outperforms　existing　fault　diagnosis　techniques.　©　2021　Elsevier　B.V.