Intelligent Fault Diagnosis Method in Controlled Rectifier Based on Support Vector Machines

2012 ◽  
Author(s):  
Hai Lan ◽  
Hong-da Liu ◽  
Wen-jie Yue ◽  
Nai-jun Shen
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Vector Machines ◽  
Diagnosis Method

The Development Research of Plug and Play Rectifier Fault Diagnosis Device

2013 ◽  
Vol 827 ◽  
pp. 309-314
Author(s):  
Fang Lu ◽  
Hong Da Liu ◽  
Wei Yuan Fan ◽  
Wen Hao Zhang ◽  
Nai Jun Shen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Support Vector ◽  
Development Research ◽  
Plug And Play ◽  
Rectifier Circuit ◽  
Vector Machines ◽  
Different Types ◽  
Diagnosis Method ◽  
Rectifier Circuits

Based on support vector machines (SVM) theory, the method of fault diagnosis for controlled rectifier circuits is expanded to study in the paper, by the basis we analysis the method can be applied to diode rectifier circuit and verified it through the experiment. In addition to this , the rectifier circuit with different types of loads are also simulated to describe the reason this method is applicable to the different types of loads. In the premise of ensuring the accuracy of the method, through the expansion of research, the fault diagnosis method has broader prospects and higher practical value.

Intelligent Fault Diagnosis in Power Plant Using Empirical Mode Decomposition, Fuzzy Feature Extraction and Support Vector Machines

2005 ◽  
Vol 293-294 ◽  
pp. 373-382 ◽  
Author(s):  
Qiao Hu ◽  
Zheng Jia He ◽  
Yanyang Zi ◽  
Zhou Suo Zhang ◽  
Yaguo Lei
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Turbo Generator ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Multi Classification

In this paper, a novel intelligent fault diagnosis method based on empirical mode decomposition (EMD), fuzzy feature extraction and support vector machines (SVM) is proposed. The method consists of two stages. In the first stage, intrinsic mode components are obtained with EMD from original signals and converted into fuzzy feature vectors, and then the mechanical fault can be detected. In the second stage, these extracted fuzzy feature vectors are input into the multi-classification SVM to identify the different abnormal cases. The proposed method is applied to the classification of a turbo-generator set under three different operating conditions. Testing results show that the classification accuracy of the proposed model is greatly improved compared with the multi-classification SVM without feature extraction and the multi-classification SVM with extracting the fuzzy feature from wavelet packets, and the faults of steam turbo-generator set can be correctly and rapidly diagnosed using this model.

Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm

2009 ◽  
Vol 224 (1) ◽  
pp. 217-229 ◽  
Author(s):  
X L Zhang ◽  
X F Chen ◽  
Z J He
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Parameter Optimization ◽  
Ant Colony Algorithm ◽  
Small Sample ◽  
Ant Colony ◽  
Support Vector ◽  
Optimal Parameters ◽  
Vector Machines ◽  
Diagnosis Method

Since support vector machines (SVM) exhibit a good generalization performance in the small sample cases, these have a wide application in machinery fault diagnosis. However, a problem arises from setting optimal parameters for SVM so as to obtain optimal diagnosis result. This article presents a fault diagnosis method based on SVM with parameter optimization by ant colony algorithm to attain a desirable fault diagnosis result, which is performed on the locomotive roller bearings to validate its feasibility and efficiency. The experiment finds that the proposed algorithm of ant colony optimization with SVM (ACO—SVM) can help one to obtain a good fault diagnosis result, which confirms the advantage of the proposed ACO—SVM approach.

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