Timely　and　effective　fault　detection　is　essential　to　ensure　the　safe　and　reliable　operation　of　wind　turbines.　However,　due　to　the　complex　kinematic　mechanisms　and　harsh　working　environments　of　wind　turbine　equipment,　it　is　difficult　to　extract　sensitive　features　and　detect　faults　from　acquired　wind　turbine　signals.　To　address　this　challenge,　a　novel　intelligent　fault　detection　scheme　for　constant-speed　wind　turbines　based　on　refined　time-shifted　multiscale　fuzzy　entropy　(RTSMFE),　supervised　isometric　mapping　(SI),　and　adaptive　chaotic　Aquila　optimization-based　support　vector　machine　(ACAOSVM)　is　proposed.　In　the　first　step,　the　RTSMFE　method　is　used　to　fully　extract　features　of　the　wind　turbine　system.　The　time-shifted　coarse-grained　construction　technique　and　a　refined　computing　technique　are　adopted　in　the　RTSMFE　method　to　enhance　the　capability　of　traditional　multiscale　fuzzy　entropy　for　measuring　the　complexity　of　signals.　Subsequently,　an　effective　manifold　learning　approach,　SI,　is　applied　to　obtain　the　important　and　low-dimensional　feature　set　from　the　high-dimensional　feature　set.　Finally,　sensitive　features　are　fed　into　the　ACAOSVM　classifier　to　identify　faults.　The　proposed　ACAO　algorithm　is　used　to　optimize　important　parameters　of　the　SVM,　thereby　improving　its　detection　performance.　Simulations　and　wind　turbine　experiments　verified　that　the　proposed　RTSMFE　outperforms　existing　entropy　techniques　in　terms　of　complexity　measurement　and　feature　extraction.　Furthermore,　the　proposed　ACAOSVM　classifier　is　superior　to　existing　advanced　classifiers　for　fault　pattern　recognition.　Finally,　the　proposed　intelligent　fault　detection　scheme　can　more　correctly　and　efficiently　detect　wind　turbine　single/hybrid　faults　than　other　recently　published　schemes.&　COPY;　2023　ISA.　Published　by　Elsevier　Ltd.　All　rights　reserved.