In　order　to　reduce　operation　and　maintenance　costs,　reliability,　and　quick　response　capability　of　multi-fault　intelligent　diagnosis　for　the　wind　turbine　system　are　becoming　more　important.　This　paper　proposes　a　rapid　data-driven　fault　diagnostic　method,　which　integrates　data　pre-processing　and　machine　learning　techniques.　In　terms　of　data　pre-processing,　fault　features　are　extracted　by　using　the　proposed　modified　Hilbert-Huang　transforms　(HHT)　and　correlation　techniques.　Then,　time　domain　analysis　is　conducted　to　make　the　feature　more　concise.　A　dimension　vector　will　then　be　constructed　by　including　the　intrinsic　mode　function　energy,　time　domain　statistical　features,　and　the　maximum　value　of　the　HHT　marginal　spectrum.　On　the　other　hand,　as　the　architecture　and　the　learning　algorithm　of　pairwise-coupled　sparse　Bayesian　extreme　learning　machine　(PC-SBELM)　are　more　concise　and　effective,　it　could　identify　the　single-and　simultaneous-fault　more　quickly　and　precisely　when　compared　with　traditional　identification　techniques　such　as　pairwise-coupled　probabilistic　neural　networks　(PC-PNN)　and　pairwise-coupled　relevance　vector　machine　(PC-RVM).　In　this　case　study,　PC-SBELM　is　applied　to　build　a　real-time　multi-fault　diagnostic　system.　To　verify　the　effectiveness　of　the　proposed　fault　diagnostic　framework,　it　is　carried　out　on　a　real　wind　turbine　gearbox　system.　The　evaluation　results　show　that　the　proposed　framework　can　detect　multi-fault　in　wind　turbine　gearbox　much　faster　and　more　accurately　than　traditional　identification　techniques.