Method for the dimension reduction of rotor fault data sets by using ISOMAP and LLE

被引：0
|
作者：
Chen P. [1 ]
Zhao R. [1 ]
Peng B. [1 ]
Li K. [1 ]
机构：
[1] Institute of Mechanical and Electrical Information Technology, College of Mechano-Electronic Engineering, Lanzhou University of Technology, Lanzhou
来源：
Zhao, Rongzhen (zhaorongzhen@lut.cn) | 1600年 / Chinese Vibration Engineering Society卷 / 36期
关键词：
Fault diagnosis; Feature extraction; Kernel method; Manifold learning;
D O I：
10.13465/j.cnki.jvs.2017.06.007
中图分类号：
学科分类号：
摘要：
The data set for fault diagnosis and decision based on machinary intelligence gives rise to the requirement of dimension reduction in data processing. The algorithms of Isometric Mapping (ISOMAP) and Locally Linear Embedding (LLE) were introduced simultaneously to mutually complement their strong points and weak points, and a new KISOMAPLLE algorithm was proposed. The algorithm can satisfy the requirement of both global distance preserving and local structure preserving ability, and has been used to reduce the dimension of typical artificial data sets and rotor fault data sets. The proposed algorithm inherits the excellent performances of ISOMAP and LLE, and can improve the classification accuracy of typical nonlinear data sets. © 2017, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：45 / 50and156
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