Fault diagnosis of transformer based on cluster analysis

2011 ◽  
Author(s):  
Feng Zhao
Keyword(s):  
Cluster Analysis ◽  
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 A Fault Diagnosis Method Based on Semi - Supervised Fuzzy C - Means Cluster Analysis

2015 ◽  
Vol 4 (2) ◽  
pp. 281-289 ◽  
Author(s):  
Su-Qun Cao ◽  
Xinggang Ma ◽  
Youfu Zhang ◽  
Limin Luo ◽  
Fupeng Yi
Keyword(s):  
Cluster Analysis ◽  
Fault Diagnosis ◽  
Fuzzy C Means ◽  
Diagnosis Method

Transformer Fault Diagnosis Based on Cluster Analysis and Statistical Theory

2010 ◽  
Vol 168-170 ◽  
pp. 1611-1614
Author(s):  
Wen Qing Zhao ◽  
Dong Xiao Niu
Keyword(s):  
Cluster Analysis ◽  
Statistical Theory ◽  
Fault Diagnosis ◽  
Concentration Distribution ◽  
Dimensional Space ◽  
The Other ◽  
Training Samples ◽  
Proposed Model ◽  
Higher Dimensional ◽  
Transformer Fault

A new method for transformer fault diagnosis based on cluster analysis and statistical theory is presented. First, the fault diagnosis results are obtained according to the distances between the state sorts of transformer. Then, the final fault diagnosis is accomplished according to the concentration distribution of typical fault gases in higher dimensional space. The proposed approach is constructing the most accuracy model from few training samples supporting. Moreover, by comparing with the other methods, it cost less time for diagnosing by the proposed model and the accuracy for transformer fault diagnosis is improved using our proposed model.

scholarly journals Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

2011 ◽  
Vol 18 (1-2) ◽  
pp. 127-137 ◽  
Author(s):  
Lingli Jiang ◽  
Yilun Liu ◽  
Xuejun Li ◽  
Anhua Chen
Keyword(s):  
Cluster Analysis ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Fuzzy Cluster ◽  
Ar Model ◽  
Fuzzy Cluster Analysis ◽  
Bearing Fault Diagnosis ◽  
Stationary Signals ◽  
Gaussian Signal ◽  
Non Gaussian

This paper proposes a new approach combining autoregressive (AR) model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

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