In　order　to　solve　the　problem　of　traditional　methods　having　low　accuracy　in　recognizing　rolling　bearings＇　faults　and　to　reduce　time　in　building　a　training　model,　this　paper　puts　forward　a　method　of　recognizing　faults　based　on　wavelet　packet　transformation　and　PCA-PSO-MSVM.　First　of　all,　we　extracted　the　energy　values　after　all　kinds　of　faulty　signals　have　been　transformed　through　wavelet　packet,　and　combined　the　index　of　the　signals＇　time-domain　characteristics　to　form　an　eigenvalue　matrix　to　be　used　as　the　characteristics　of　SVM　sample　training.　Then　by　using　PCA　(principal　component　analysis)　method,　we　eliminated　the　redundant　characteristics　to　increase　the　efficiency　of　the　model　structure.　Afterwards,　through　MSVM　algorithm　and　one-to-one　strategy,　we　carried　out　final　multi-classification　recognition　of　the　mixed　faults　of　rolling　bearings,　with　PSO　(particle　swarm　optimization)　algorithm　also　adopted　for　optimizing　SVM　parameters　to　improve　the　accuracy　of　classifications.　The　experiment　adopted　the　data　of　rolling　bearings　provided　by　Case　Western　Reserve　University.　As　shown　in　the　experimental　results,　this　method　has　high　accuracy　and　efficiency.　©　2016　IEEE.