Study of Fault Diagnosis Based On Manifold Learning


Yilin He;Guangbin Wang;Fuze Xu;


Characteristic signals in rotating machinery fault diagnosis with the issues of complex and difficult to deal with, while the use of non-linear manifold learning method can effectively extract low-dimensional manifold characteristics embedded in the high-dimensional non-linear dataIt greatly maintains the overall geometric structure of the signals and improves the efficiency and reliability of the rotating machinery fault diagnosisAccording to the development prospects of manifold learning, this paper describes four classical manifold learning methods and each advantages and disadvantagesIt reviews the research status and application of fault diagnosis based on manifold learning, as well as future direction of researches in the field of manifold learning fault diagnosis.


Fault diagnosis,Manifold learning,Dimensionality reduction,Feature Extraction


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Total: 21 articles

  • [1] XIAO Jian-hua (Inst. of Int. Tech. & Sys., Wuyi Univ., Jiangmen 529020, China), Condition Monitoring Technology for Complicated Equipment Based on Kernel Method, Journal of Wuyi University(Natural Science Edition),
  • [2] Meng Deyu1,2, Xu Zongben1, and Dai Mingwei2 1(Institute for Information and System Science, Xi'an Jiaotong University, Xi'an 710049) 2(School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049), A New Supervised Manifold Learning Method, Journal of Computer Research and Development,
  • [3] TAN Lu WU Yi YI Dong-Yun (Department of Mathematic and System Science,National University of Defense Technology,Changsha 410073), Intrinsinc Dimension Estimation Based on LLE, Pattern Recognition and Artificial Intelligence,
  • [4] JIANG Run, ZHOU Ji-liu, LEI Gang, LI Xiao-hua(School of Computer, Sichuan University, Chengdu 610064), α-based Supervised Orthogonal Projection Reduction by Affinity, Computer Engineering,


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