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.

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 Multifunctional Prosthesis Control with Simulation of Myoelectric Signals

2020 ◽  
Author(s):  
João Fermeiro ◽  
Filipa Moreira ◽  
José Pombo ◽  
Rosário Calado ◽  
Sílvio Mariano
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Support Vector Machines ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Support Vector ◽  
Signal Conditioning ◽  
Myoelectric Signals ◽  
Emg Signal ◽  
Vector Machines

The skeletal muscle activation generates electric signals called myoelectric signals. In recent years a strong scientific activity has been developed in the recognition of limb movements from electromyography (EMG) signals recorded from non-invasive (surface) electrodes, in order to design systems for prosthetic control. Surface EMG acquire the activation of surrounding muscles and for that reason the obtained signal needs to be conditioned and processed, with pattern recognition techniques for extraction and classification. In this work EMG signals were acquired for two hand movements, “hand close” and “hand open”.  The EMG electrodes were placed on the forearm  and positioned over the extensor digitorum muscle, for the “hand open” and flexor digitorum muscle, for the “hand close”. Using MATLAB software the signal conditioning, feature extraction and classification were performed. The feature extraction process was carried with the Wavelet Packet Transform (WPT) technique and the classification process was done with two different techniques for comparison purposes, Neural Networks (NN) and Support Vector Machines (SVM). The results show that the SVM classifier used presented better classification performance compared to NN classifier used. Keywords: EMG, Signal conditioning, Wavelet Packet Transform (WPT), Neural Networks (NN), Support Vector Machines (SVM)

Residual Subsidence Prediction of Abandoned Mine Goaf Based on Wavelet Support Vector Machines

2012 ◽  
Vol 524-527 ◽  
pp. 330-336 ◽  
Author(s):  
Zheng Shuai Wang ◽  
Ka Zhong Deng
Keyword(s):  
Support Vector Machines ◽  
Prediction Model ◽  
Abandoned Mine ◽  
Support Vector ◽  
Stochastic Fluctuation ◽  
Stochastic Signal ◽  
Multi Resolution Analysis ◽  
Svm Model ◽  
Vector Machines ◽  
Resolution Analysis

The prediction of residual subsidence is the fundament of stability evaluation of buildings foundation in the abandoned mine goaf, so how to get the residual subsidence with high precision is significant to reclaim the goaf for buildings. In this paper, a novel prediction model named wavelet support vector machines (WT-SVM) is proposed to forecast residual subsidence. Aiming at the stochastic fluctuation of the subsidence series, the measured data of residual subsidence were separated into components, namely, trend, oscillating sequence and stochastic signal, via wavelet multi-resolution analysis; then, the prediction model was established based on SVM regression algorithm, respectively, and the sum of the total corresponding prediction values were regarded as the final results of the residual subsidence. The predicting results of WT-SVM, SVM and BP neural network (BP-NN) were compared by a case study. The conclusions are as follows: WT-SVM model is obviously superior to other models in terms of the aspects of prediction precision, step and stability, which indicates the feasibility and effectivity of WT-SVM in predicting residual subsidence of the abandoned mine goaf.

Structure Fault Diagnosis of Tower Crane Based on Wavelet Packet Analysis and Support Vector Machines

2013 ◽  
Vol 325-326 ◽  
pp. 294-298
Author(s):  
Sheng Chun Wang ◽  
Rong Sheng Shen ◽  
Shi Jun Song ◽  
Yan Tian
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Structural Damage ◽  
Wavelet Packet ◽  
Fault Classification ◽  
Support Vector ◽  
Analysis Model ◽  
Element Analysis ◽  
Tower Crane ◽  
Vector Machines

First establish a dynamic model of tower crane in the load lifting process, the lifting load is solved under two work conditions.Then establish the FEM(finite element analysis) model of the tower crane under the normal and the damage condition. Get the dynamic displacement of the normal and the damage status under the lifting dynamic load. With wavelet packet decomposition and SVM(Support vector machines) multi-classification algorithm, a multi-fault classifier is constructed, and applied to the fault diagnosis of tower body. The results of the study show that the multi-fault classifier has such advantages as simple algorithm and excellent capability of fault classification, and it can not only diagnose the structural damage status, but also determine the positions of structural damage. This will be a new search on tower crane structural health diagnosis.

Sign in / Sign up

Export Citation Format

Share Document