Pitch　system　fault　prediction　and　improvement　of　prediction　accuracy　are　key　technologies　for　wind　power　devel-opment,　which　ensure　safe　operation　of　the　grid　and　effectively　reduces　operation　and　maintenance　costs.　The　Supervisory　con-trol　and　data　acquisition　(SCADA)　system　data　is　analyzed　and　processed　to　extract　the　associated　parameters,　i.e.　output　power,　wind　speed,　pitch　angle,　and　rotor　speed.　A　Back　Propagation　(BP)　neural　network　is　used　to　train　the　system,　taking　into　account　the　volatility　and　uncertainty　of　wind　turbine　param-eters,　and　a　regression　prediction　model　with　a　support　vector　regression　(SVR)　algorithm　is　also　used　for　training.　A　pitch　failure　prediction　model　is　established　to　predict　the　operation　of　the　pitch　system,　which　is　used　to　develop　a　reasonable　operation　and　maintenance　plan.　Through　the　system　simulation,　the　prediction　model　performance　index,　error-index,　and　output　data　graphics　are　compared　and　analyzed.　　©　2023　IEEE.