A Two-Stage Manifold Learning Framework for Machinery Fault Diagnosis

2017 ◽  
Author(s):  
Zuqiang Su ◽  
Haitao Xu ◽  
Jiufei Luo
Keyword(s):  
Fault Diagnosis ◽  
Manifold Learning ◽  
Two Stage ◽  
Learning Framework ◽  
Machinery Fault Diagnosis

Study of Fault Diagnosis Based on Manifold Learning

2013 ◽  
Vol 312 ◽  
pp. 650-654 ◽  
Author(s):  
Yi Lin He ◽  
Guang Bin Wang ◽  
Fu Ze Xu
Keyword(s):  
Fault Diagnosis ◽  
Manifold Learning ◽  
Linear Manifold ◽  
Rotating Machinery ◽  
Dimensional Manifold ◽  
Advantages And Disadvantages ◽  
Non Linear ◽  
Future Direction ◽  
Low Dimensional ◽  
Machinery Fault Diagnosis

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 data. It greatly maintains the overall geometric structure of the signals and improves the efficiency and reliability of the rotating machinery fault diagnosis. According to the development prospects of manifold learning, this paper describes four classical manifold learning methods and each advantages and disadvantages. It 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 method based on a new manifold learning framework

2018 ◽  
Vol 34 (6) ◽  
pp. 3413-3427 ◽  
Author(s):  
Zuqiang Su ◽  
Haitao Xu ◽  
Jiufei Luo ◽  
Kai Zheng ◽  
Yi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Manifold Learning ◽  
Learning Framework ◽  
Diagnosis Method

Machinery fault diagnosis using supervised manifold learning

2009 ◽  
Vol 23 (7) ◽  
pp. 2301-2311 ◽  
Author(s):  
Quansheng Jiang ◽  
Minping Jia ◽  
Jianzhong Hu ◽  
Feiyun Xu
Keyword(s):  
Fault Diagnosis ◽  
Manifold Learning ◽  
Machinery Fault Diagnosis

Deep transfer learning with limited data for machinery fault diagnosis

2021 ◽  
Vol 103 ◽  
pp. 107150
Author(s):  
Te Han ◽  
Chao Liu ◽  
Rui Wu ◽  
Dongxiang Jiang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Limited Data ◽  
Machinery Fault Diagnosis
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