Intelligent Fault Diagnosis Based on a Hybrid Multi-Class Support Vector Machines and Case-Based Reasoning Approach

2013 ◽  
Vol 10 (8) ◽  
pp. 1727-1734 ◽  
Author(s):  
Sangwon Lee ◽  
Kwang-Kyu Seo
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Intelligent Fault Diagnosis ◽  
Vector Machines ◽  
Case Based

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.

scholarly journals Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1298 ◽  
Author(s):  
Kun He ◽  
Zhijun Yang ◽  
Yun Bai ◽  
Jianyu Long ◽  
Chuan Li
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Vector Machines ◽  
3D Printers
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