MODIFIED LAPLACIAN EIGENMAP ETHOD FOR FAULT DIAGNOSIS  

作　　者：JIANG Quansheng JIA Minping HU Jianzhong XU Feiyun 

机构地区：[1]School of Mechanical Engineering, Southeast University, Nanjing 211189, China [2]Department of Physics, Chaohu University,Chaohu 238000, China

出　　处：《Chinese Journal of Mechanical Engineering》2008年第3期90-93,共4页中国机械工程学报（英文版）

基　　金：National Hi-tech Research Development Program of China(863 Program,No.2007AA04Z421);National Natural Science Foundation of China(No.50475078,No.50775035)

摘　　要：A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.

关 键 词：Laplacian eigenmap Kernel trick Fault diagnosis Manifold learning 

分 类 号：TH123[机械工程—机械设计及理论]