Application of nonlinear feature extraction and least square support vector machines for fault diagnosis of chemical process

2010 ◽  
Vol 30 (1) ◽  
pp. 236-239 ◽  
Author(s):  
Liang XU
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Chemical Process ◽  
Least Square ◽  
Support Vector ◽  
Vector Machines ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

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 Bearings Based on Energy Levels in Frequency Bands Using Wavelet and Support Vector Machines (SVM)

2019 ◽  
Vol 3 (1) ◽  
pp. 11 ◽  
Author(s):  
Seyed Yadavar Nikravesh ◽  
Hossein Rezaie ◽  
Margaret Kilpatrik ◽  
Hossein Taheri
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Wavelet Packet ◽  
Energy Levels ◽  
Support Vector ◽  
Frequency Bands ◽  
Multi Resolution Analysis ◽  
Vector Machines ◽  
Resolution Analysis

In this paper, a new method was introduced for feature extraction and fault diagnosis in bearings based on wavelet packet decomposition and analysis of the energy in different frequency bands. This method decomposes a signal into different frequency bands using different types of wavelets and performs multi-resolution analysis to extract different features of the signals by choosing energy levels in different frequency bands. The support vector machines (SVM) technique was used for faults classifications. Daubechies, biorthogonal, coiflet, symlet, Meyer, and reverse Meyer wavelets were used for feature extraction. The most appropriate decomposition level and frequency band were selected by analyzing the variation in the signal’s energy level. The proposed approach was applied to the fault diagnosis of rolling bearings, and testing results showed that the proposed approach can reliably identify different fault categories and their severities. Moreover, the effectiveness of the proposed feature selection and fault diagnosis method was significant based on the similarity between the wavelet packet and the signal, and effectively reduced the influence of the signal noise on the classification results.

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