Computer　numerical　control　(CNC)-based　systems　are　key　functional　components　of　industrial　manufacturing　installations,　and　the　servo　drive　system　is　the　main　functional　component　of　CNC　systems.　The　complex　working　environment　of　industrial　facilities　will　lead　to　the　frequent　failure　of　servo　drive　systems,　and　effective　fault　diagnosis　measures　are　important　to　ensure　the　normal　operation　of　CNC　machine　tools.　In　this　paper,　the　application　of　fault　diagnosis　methods　in　servo　drive　systems　is　considered,　and　a　method　suitable　for　high-dimensional　data　of　CNC　systems　is　presented.　Using　data　collected　by　a　physical　system　and　related　indicators,　the　technique　can　be　used　to　identify　hidden　fault　characteristics　in　the　data　and　to　diagnose　the　fault　types.　The　core　of　this　method　is　the　self-organizing　map　neural　network,　which　uses　unsupervised　competitive　learning　to　cluster　data　with　different　characteristics,　find　the　winning　neurons,　and　diagnose　the　fault.　The　introduction　of　feature　standardization　in　the　map＇s　initialization　stage　can　accelerate　the　model＇s　training　convergence　speed　and　reduce　the　feature　weight　deviation.　At　the　same　time,　principal　component　analysis　is　introduced　to　balance　the　influence　of　different　feature　scales,　enhance　the　features　of　fault　data,　reduce　the　data　dimensionality,　and　improve　the　interpretability　of　the　model.　A　comparison　with　the　conventional　algorithm　and　testing　various　fault　datasets　shows　that　the　proposed　method　exhibits　improved　performance　when　processing　high-dimensional　data　and　its　enhancement　fault　recognition　effect　is　verified.