The　vibration　signal　on　the　surface　of　distribution　transformer　contains　rich　state　information　of　the　winding　and　iron　core,　which　directly　reflects　the　working　conditions　of　the　winding　and　iron　core.　The　Hilbert-Huang　Transform　(HHT)　band-pass　filtering　is　adopted　to　extract　the　main　components　of　the　distribution　transformer　vibration　signal.　The　100　Hz　and　150~1　000　Hz　signals　are　obtained,　which　represent　the　working　conditions　of　the　winding　and　iron　core,　respectively.　The　load　current　curve　fitting　method　is　used　to　extract　the　features　of　the　vibration　signal,　the　100Hz　vibration　amplitude　of　the　winding　under　specified　load　is　estimated　from　the　measured　vibration　signal,　and　the　feature　vectors　of　winding　vibration　are　constructed.　The　bi-spectrum　analysis　combined　with　Singular　Value　Decomposition　(SVD)　that　has　good　generalization　capability　and　robustness　is　adopted　to　represent　the　vibration　features　of　the　iron　core.　Large　number　of　feature　vectors　were　extracted　from　various　fault　vibration　signals　tested　in　the　laboratory,　such　as　winding　looseness,　winding　deformation,　core　looseness,　core　two-point　grounding,　poor　core　grounding　and　so　on.　The　Support　Vector　Machine　(SVM)　based　on　information　fusion　was　used　to　achieve　the　condition　identification　of　the　winding　and　core.　The　results　verify　the　effectiveness　and　accuracy　of　the　proposed　algorithm.　©　2016,　Science　Press.　All　right　reserved.