The　computer　numerically　controlled　(CNC)　system　is　the　key　functional　component　of　CNC　machine　tool　control　systems,　and　the　servo　drive　system　is　an　important　part　of　CNC　systems.　The　complex　working　environment　will　lead　to　frequent　failure　of　servo　drive　systems.　Taking　effective　health　management　measures　is　the　key　to　ensure　the　normal　operation　of　CNC　machine　tools.　In　this　paper,　the　comprehensive　effect　of　fault　prediction　and　fault　diagnosis　is　considered　for　the　first　time,　and　a　health　management　system　for　machine　tool　servo　drive　systems　is　proposed　and　applied　to　operation　and　maintenance　management.　According　to　the　data　collected　by　the　system　and　related　indicators,　the　technology　can　predict　the　state　trend　of　equipment　operation,　identify　the　hidden　fault　characteristics　in　the　data,　and　further　diagnose　the　fault　types.　A　health　management　system　mainly　includes　fault　prediction　and　fault　diagnosis.　The　core　of　fault　prediction　is　the　gated　recurrent　unit　(GRU).　The　attention　mechanism　is　introduced　into　a　GRU　neural　network,　which　can　solve　the　long-term　dependence　problem　and　improve　the　model　performance.　At　the　same　time,　the　Nadam　optimizer　is　used　to　update　the　model　parameters,　which　improves　the　convergence　speed　and　generalization　ability　of　the　model　and　makes　it　suitable　for　solving　the　prediction　problem　of　large-scale　data.　The　core　of　fault　diagnosis　is　the　self-organizing　mapping　(SOM)　neural　network,　which　performs　cluster　analysis　on　data　with　different　characteristics,　to　realize　fault　diagnosis.　In　addition,　feature　standardization　and　principal　component　analysis　(PCA)　are　introduced　to　balance　the　influence　of　different　feature　scales,　enhance　the　feature　of　fault　data,　and　achieve　data　dimensionality　reduction.　Compared　with　the　other　two　algorithms　and　their　improved　versions,　the　superiority　of　the　health　management　system　with　high-dimensional　data　and　the　enhancement　effect　of　fault　identification　are　verified.　The　relative　relationship　between　fault　prediction　and　diagnosis　is　further　revealed,　and　the　adjustment　idea　of　the　production　plan　is　provided　for　decision　makers.　The　rationality　and　effectiveness　of　the　system　in　practical　application　are　verified　by　a　series　of　tests　of　fault　data　sets.