In　fault　diagnosis　of　rotating　machinery,　the　strong　noise　and　outliers　interference　are　usually　contained　in　the　vibration　signals.　After　fault　feature　extraction,　the　method　of　traditional　support　vector　machine　(SVM)　for　the　pattern　recognition　causes　the　fuzzy　of　optimal　hyperplane　and　affects　the　classification　results.　So　a　fuzzy　C-means　(FCM)　clustering　algorithm　was　introduced　in　this　paper.　FCM　was　used　to　solve　the　problem　of　fuzzy　membership.　However,　the　FCM　had　its　own　defects.　The　clustering　result　was　sensitive　to　the　initial　center,　and　often　cannot　achieve　the　result　of　the　global　optimal.　Improved　by　particle　swarm　optimization　(PSO)　which　has　advantages　of　global　optimization　search,　the　FCM　achieved　better　fuzzy　memberships　for　each　sample.　So,　the　fault　diagnosis　algorithm　of　rotating　machinery　based　on　the　improved　fuzzy　support　vector　machine　(FSVM)　was　proposed.　First,　fault　features　were　extracted　by　using　the　empirical　mode　decomposition　(EMD).　Second,　the　problem　of　fuzzy　membership　was　solved　by　using　FCM　which　was　optimized　by　PSO.　At　last　the　fuzzy　memberships　were　put　into　SVM,　the　improved　FSVM　was　founded　and　fault　recognition　was　realized.　Results　of　the　experiment　show　that　the　improved　FSVM　has　better　anti-noise　performance　and　the　classification　effect　is　better　than　that　of　the　traditional　FSVM　algorithm.　©　2015,　Beijing　University　of　Technology.　All　right　reserved.