Fault Diagnosis Method for Rotating Machinery Base on Manifold Learning and K-Nearest Neighbor Classifier
SONG Tao1，TANG Bao-ping1，LI Feng2
1. The State Key Laboratory of Mechanical Transmission, Chongqing University ,Chongqing 400044,China2. School of Manufacturing Science and Engineering, Sichuan University, Chengdu 610065,China
Abstract：With the problems of needing manual intervention 、low accuracy and difficult to obtain fault samples for rotating machinery fault diagnosis, a fault diagnosis method is proposed based on manifold learning and K-Nearest Neighbor Classifier(KNNC).Multi-domain information entropy for vibration signal is extracted to reflect the working status fully and construct high-dimensional characteristic sets. Then the second feature extraction property of nonlinear manifold learning algorithm Orthogonal Neighborhood Preserving Embedding(ONPE) is used for dimensionality reduction and make the characters get better clustering property. Finally, improved KNNC is used for Pattern classification and it is more suitable for small sample classification. The diagnostic case for bearing proved the effectiveness of the model.
2013, Vol. 32 
