Currently,　in　the　research　on　fault　information　of　high-voltage　circuit　breakers　(HVCBs)　based　on　multi-sensor　data,　issues　such　as　shallow　feature　assessment　and　unclear　feature　selection　rules　exist,　leading　to　a　decrease　in　fault　identification　accuracy　due　to　redundant　characteristics.　To　address　this,　this　paper　proposes　a　novel　feature　selection　method　based　on　GA-Kmeans.　This　method　encodes　the　original　feature　space　into　binary　format　and　employs　the　clustering　accuracy　of　the　Kmeans　model　as　the　fitness　function　to　obtain　a　high-quality　feature　subset　that　maximally　represents　and　distinguishes　faults.　By　combining　different　feature　selection　methods　with　fault　diagnosis　models,　six　typical　application　scenarios　are　constructed.　Results　indicate　that　compared　to　traditional　methods　such　as　Relief-F,　KPCA,　GA-SVM　for　feature　selection,　and　SVM　for　fault　diagnosis,　the　proposed　GA-Kmeans　method　reduces　the　dimensionality　of　the　original　feature　space　and　employs　the　Kmeans　clustering　algorithm　as　the　diagnostic　model,　achieving　a　final　diagnostic　accuracy　of　95.29%.　This　method　outperforms　others,　with　a　37.24%　higher　diagnostic　accuracy　than　SVM　under　the　original　feature　space,　and　a　decrease　of　47.90%　in　standard　deviation.　This　validates　the　necessity　of　feature　selection　and　the　superiority　of　the　proposed　method,　providing　a　reliable　and　stable　diagnostic　basis　for　subsequent　mechanical　fault　diagnosis　of　HVCBs.　©　2024　IOP　Publishing　Ltd.　All　rights,　including　for　text　and　data　mining,　AI　training,　and　similar　technologies,　are　reserved.