THE APPLICATION OF SOME NON-LINEAR METHODS IN ROTATING MACHINERY FAULT DIAGNOSIS

2001 ◽  
Vol 15 (4) ◽  
pp. 697-705 ◽  
Author(s):  
W.J. WANG ◽  
J. CHEN ◽  
X.K. WU ◽  
Z.T. WU
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Non Linear ◽  
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.

Digital Twin for rotating machinery fault diagnosis in smart manufacturing

2018 ◽  
Vol 57 (12) ◽  
pp. 3920-3934 ◽  
Author(s):  
Jinjiang Wang ◽  
Lunkuan Ye ◽  
Robert X. Gao ◽  
Chen Li ◽  
Laibin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Machinery Fault Diagnosis
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