Bearing Fault Diagnosis Based on Feature Weighted FCM Cluster Analysis

2008 ◽  
Author(s):  
Sui Wentao ◽  
Lu Changhou ◽  
Zhang Dan
Keyword(s):  
Cluster Analysis ◽  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

A Bearing Intelligent Fault Diagnosis Method Based on Cluster Analysis

2012 ◽  
Vol 152-154 ◽  
pp. 1628-1633 ◽  
Author(s):  
Su Qun Cao ◽  
Xiao Ming Zuo ◽  
Ai Xiang Tao ◽  
Jun Min Wang ◽  
Xiang Zhi Chen
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Fault Diagnosis ◽  
Test Data ◽  
Rolling Bearing ◽  
Intelligent Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Type ◽  
Diagnosis Method

In recent years, machine learning techniques have been widely used in intelligent fault diagnosis field. As a major unsupervised learning technology, cluster analysis plays an important role in fault intelligent diagnosis based on machine learning. In rolling bearing fault diagnosis, the traditional spectrum analysis method usually adopts the resonant demodulation technology, but when the inner circle, rolling body or multi-point faults produce composite modulation, it is difficulty to identify the fault type from demodulation spectral lines. According to this, a novel rolling bearing fault diagnosis method based on KFCM (Kernel-based Fuzzy C-Means) cluster analysis is proposed. Through clustering on test data and the known samples, the memberships of test data are obtained. From these, the rolling bearing fault type can be determined. Experimental results show that this method is effective.

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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