Rolling bearing fault diagnosis based on one-dimension blind source separation

2014 ◽  
Vol 27 (6) ◽  
pp. 535-542 ◽  
Author(s):  
Xiaohui Li ◽  
Pan Fu
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Rolling Bearing ◽  
One Dimension ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Rolling Bearing Fault Diagnosis Based on Blind Source Separation

2012 ◽  
Vol 217-219 ◽  
pp. 2546-2549 ◽  
Author(s):  
Chang Zheng Chen ◽  
Qiang Meng ◽  
Hao Zhou ◽  
Yu Zhang
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Rolling Bearing ◽  
New Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

This document presents fault diagnosis method of rolling bearing based on blind source separation. The algorithm based on fast ICA is improved to separate fault signals according to the rolling bearing’s fault characteristics. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.

Bearing Fault Diagnosis of the Gearbox Using Blind Source Separation

2020 ◽  
Author(s):  
Hong Zhong ◽  
Jingxing Liu ◽  
Liangmo Wang ◽  
Yang Ding ◽  
Yahui Qian
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Research on Application of Blind Source Separation in Rolling Bearing Fault Diagnosis Based on Particle Swarm Optimization

2014 ◽  
Vol 971-973 ◽  
pp. 1321-1324
Author(s):  
Hao Zhou ◽  
Chang Zheng Chen ◽  
Xian Ming Sun ◽  
Huan Liu
Keyword(s):  
Signal Processing ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Particle Swarm ◽  
Rolling Bearing ◽  
Swarm Optimization ◽  
Bearing Fault Diagnosis ◽  
Research On Application

This paper presents blind source separation of rolling bearing based on particle swarm optimization. The algorithm combines the advantages of both blind source separation and particle swarm optimization. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method
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