Research Status of Fault Diagnosis Based on Support Vector Machine

2013 ◽  
Vol 475-476 ◽  
pp. 787-791
Author(s):  
Li Mei Liu ◽  
Jian Wen Wang ◽  
Ying Guo ◽  
Hong Sheng Lin
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Small Sample ◽  
Learning Ability ◽  
Practical Significance ◽  
Support Vector ◽  
Limited Information ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Good Learning

Support vector machine has good learning ability and it is good to perform the structural risk minimization principle of statistical learning theory and its application in fault diagnosis of the biggest advantages is that it is suitable for small sample decision. Its nature of learning method is under the condition of limited information to maximize the implicit knowledge of classification in data mining and it is of great practical significance for fault diagnosis. This paper analyzed and summarized the present situation of application of support vector machine in fault diagnosis and made a meaningful exploration on development direction of the future.

scholarly journals Sparse Representation and SVM Diagnosis Method Inter-Turn Short-Circuit Fault in PMSM

2019 ◽  
Vol 9 (2) ◽  
pp. 224 ◽  
Author(s):  
Siyuan Liang ◽  
Yong Chen ◽  
Hong Liang ◽  
Xu Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Permanent Magnet ◽  
Short Circuit ◽  
Small Sample ◽  
Support Vector ◽  
Feature Vectors ◽  
Diagnosis Method ◽  
Short Circuit Fault

Permanent magnet synchronous motors (PMSM) has the advantages of simple structure, small size, high efficiency, and high power factor, and a key dynamic source and is widely used in industry, equipment and electric vehicle. Aiming at its inter-turn short-circuit fault, this paper proposes a fault diagnosis method based on sparse representation and support vector machine (SVM). Firstly, the sparse representation is used to extract the first and second largest sparse coefficients of both current signal and vibration signals, and then they are composed into four-dimensional feature vectors. Secondly, the feature vectors are input into the support vector machine for fault diagnosis, which is suitable for small sample. Experiments on a permanent magnet synchronous motor with artificially set inter-turn short-circuit fault and a normal one showed that the method is feasible and accurate.

Application of Genetic Arithmetic and Support Vector Machine in Prediction of Compression Index of Clay

2013 ◽  
Vol 438-439 ◽  
pp. 1167-1170
Author(s):  
Xu Chao Shi ◽  
Ying Fei Gao
Keyword(s):  
Support Vector Machine ◽  
Influence Factors ◽  
Support Vector ◽  
Compression Index ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Structural Risk Minimization Principle ◽  
Svm Model ◽  
Genetic Arithmetic ◽  
Practical Tool

The compression index is an important soil property that is essential to many geotechnical designs. As the determination of the compression index from consolidation tests is relatively time-consuming. Support Vector Machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. Considering the fact that parameters in SVM model are difficult to be decided, a genetic SVM was presented in which the parameters in SVM method are optimized by Genetic Algorithm (GA). Taking plasticity index, water content, void ration and density of soil as primary influence factors, the prediction model of compression index based on GA-SVM approach was obtained. The results of this study showed that the GA-SVM approach has the potential to be a practical tool for predicting compression index of soil.

scholarly journals Wavelet Correlation Feature Scale Entropy and Fuzzy Support Vector Machine Approach for Aeroengine Whole-Body Vibration Fault Diagnosis

2013 ◽  
Vol 20 (2) ◽  
pp. 341-349 ◽  
Author(s):  
Cheng-Wei Fei ◽  
Guang-Chen Bai
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Whole Body Vibration ◽  
Learning Ability ◽  
Whole Body ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Vibration Signals ◽  
Body Vibration ◽  
Feature Scale

In order to correctly analyze aeroengine whole-body vibration signals, Wavelet Correlation Feature Scale Entropy (WCFSE) and Fuzzy Support Vector Machine (FSVM) (WCFSE-FSVM) method was proposed by fusing the advantages of the WCFSE method and the FSVM method. The wavelet coefficients were known to be located in high Signal-to-Noise Ratio (S/N or SNR) scales and were obtained by the Wavelet Transform Correlation Filter Method (WTCFM). This method was applied to address the whole-body vibration signals. The WCFSE method was derived from the integration of the information entropy theory and WTCFM, and was applied to extract the WCFSE values of the vibration signals. Among the WCFSE values, theWFSE1andWCFSE2values on the scale 1 and 2 from the high band of vibration signal were believed to acceptably reflect the vibration feature and were selected to construct the eigenvectors of vibration signals as fault samples to establish the WCFSE-FSVM model. This model was applied to aeroengine whole-body vibration fault diagnosis. Through the diagnoses of four vibration fault modes and the comparison of the analysis results by four methods (SVM, FSVM, WESE-SVM, WCFSE-FSVM), it is shown that the WCFSE-FSVM method is characterized by higher learning ability, higher generalization ability and higher anti-noise ability than other methods in aeroengine whole-vibration fault analysis. Meanwhile, this present study provides a useful insight for the vibration fault diagnosis of complex machinery besides an aeroengine.

scholarly journals Fuzzy reliability analysis based on support vector machine

2021 ◽  
Author(s):  
xiao bo Nie ◽  
Haibin Li ◽  
Hongxia Chen ◽  
Ruying Pang ◽  
Honghua Sun
Keyword(s):  
Support Vector Machine ◽  
Probability Distribution ◽  
Reliability Analysis ◽  
Small Sample ◽  
Learning Ability ◽  
Support Vector ◽  
Fuzzy Reliability ◽  
Performance Function ◽  
Sample Data ◽  
Distribution Parameters

Abstract For a structure with implicit performance function structure and less sample data, it is difficult to obtain accurate probability distribution parameters by traditional statistical analysis methods. To address the issue, the probability distribution parameters of samples are often regarded as fuzzy numbers. In this paper, a novel fuzzy reliability analysis method based on support vector machine is proposed. Firstly, the fuzzy variable is converted into an equivalent random variable, and the equivalent mean and equivalent standard deviation are calculated. Secondly, the support vector regression machine with excellent small sample learning ability is used to train the sample data. Subsequently, and the performance function is approximated. Finally, the Monte Carlo method is used to obtain fuzzy reliability. Numerical examples are investigated to demonstrate the effectiveness of the proposed method, which provides a feasible way for fuzzy reliability analysis problems of small sample data.

Support Vector Machine-Based Aqueduct Safety Assessment

2011 ◽  
Vol 368-373 ◽  
pp. 531-536
Author(s):  
Qiang Qu ◽  
Ming Qi Chang ◽  
Lei Xu ◽  
Yue Wang ◽  
Shao Hua Lu
Keyword(s):  
Support Vector Machine ◽  
Safety Assessment ◽  
Optimal Solution ◽  
Indicator System ◽  
Small Sample ◽  
Assessment Model ◽  
Irrigation District ◽  
Support Vector ◽  
Risk Minimization ◽  
Safety Standards

According to water power, structure and foundation conditions of aqueduct, it has established aqueduct safety assessment indicator system and standards. Based on statistical learning theory, support vector machine shifts the learning problems into a convex quadratic programming problem with structural risk minimization criterion, which could get the global optimal solution, and be applicable to solving the small sample, nonlinearity classification and regression problems. In order to evaluate the safety condition of aqueduct, it has established the aqueduct safety assessment model which is based on support vector machine. It has divided safety standards into normal, basically normal, abnormal and dangerous. According to the aqueduct safety assessment standards and respective evaluation level, the sample set is generated randomly, which is used to build a pair of classifier with many support vectors. The results show that the method is feasible, and it has a good application prospect in irrigation district canal building safety assessment.

A Discrimination Method of Saturated Sand Liquefaction Possibility Based on Support Vector Machine

2014 ◽  
Vol 509 ◽  
pp. 38-43
Author(s):  
Zhong Jie Fan ◽  
Yan Qiu Leng ◽  
Yong Long Xu ◽  
Zheng Jiang Meng ◽  
Ji Wei Xu
Keyword(s):  
Support Vector Machine ◽  
Influence Factors ◽  
Kernel Functions ◽  
Small Sample ◽  
Support Vector ◽  
Saturated Sand ◽  
Risk Minimization ◽  
Sand Liquefaction ◽  
Discriminant Model ◽  
Svm Model

Based on the analysis of influence factors of saturated sand, this paper expounds the limitations of traditional evaluation of liquefaction, and introduces the criterion of support vector machine (SVM) based on the principle of structural risk minimization. According to the main influence factors of sand liquefaction, a SVM discriminant model of sand liquefaction with different kernel functions is established. Through studying small sample data, this model can establish nonlinear mapping relationship between influence factors and liquefaction type. On the basis of seismic data, a radial based kernel function is selected to predict sand liquefaction type. The research results show that the predicted magnitude is identical with the actual result, to prove that it is effective to apply this SVM model to evaluate the level of sand liquefaction.

Power Transformer Fault Diagnosis Based on Least Squares Support Vector Machine and Particle Swarm Optimization

2011 ◽  
Vol 50-51 ◽  
pp. 624-628
Author(s):  
Xin Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Power Transformer ◽  
Particle Swarm ◽  
Support Vector ◽  
Risk Minimization ◽  
Swarm Optimization ◽  
Transformer Fault

Dissolved gas analysis (DGA) is an important method to diagnose the fault of power t ransformer. Least squares support vector machine (LS-SVM) has excellent learning, classification ability and generalization ability, which use structural risk minimization instead of traditional empirical risk minimization based on large sample. LS-SVM is widely used in pattern recognition and function fitting. Kernel parameter selection is very important and decides the precision of power transformer fault diagnosis. In order to enhance fault diagnosis precision, a new fault diagnosis method is proposed by combining particle swarm optimization (PSO) and LS-SVM algorithm. It is presented to choose σ parameter of kernel function on dynamic, which enhances precision rate of fault diagnosis and efficiency. The experiments show that the algorithm can efficiently find the suitable kernel parameters which result in good classification purpose.

Support Vector Machine Ensemble Based on Choquet Integral for Financial Distress Prediction

2015 ◽  
Vol 29 (04) ◽  
pp. 1550016 ◽  
Author(s):  
Xihua Li ◽  
Fuqiang Wang ◽  
Xiaohong Chen
Keyword(s):  
Support Vector Machine ◽  
Financial Distress ◽  
Choquet Integral ◽  
Radical Change ◽  
Ensemble Method ◽  
Support Vector ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Financial Distress Prediction ◽  
Distress Prediction

Due to the radical change in both Chinese and global economic environment, it is essential to develop a practical model to predict financial distress. The support vector machine (SVM), a new outstanding learning machine based on the statistical learning theory, embodying the principle of structural risk minimization instead of empirical risk minimization principle, is a promising method for such financial distress prediction. However, to some extent, the performance of single classifier depends on the sample's pattern characteristics and each single classifier has its own uncertainty. Using the ensemble methods to predict financial distress becomes a rising trend in this field. This research puts forward a SVM ensemble based on the Choquet integral for financial distress prediction in which Bagging algorithm is used to generate new training sets. The proposed ensemble method can be expressed as "Choquet + Bagging + SVMs". With real data from Chinese listed companies, an experiment is carried out to compare the performance of single classifiers with the proposed ensemble method. Empirical results indicate that the proposed ensemble of SVMs based on the Choquet integral for financial distress prediction has higher average accuracy and stability than single SVM classifiers.

Support Vector Machine and its Application Status

2014 ◽  
Vol 1030-1032 ◽  
pp. 1814-1817
Author(s):  
Lan Lan Kang ◽  
Wen Liang Cao
Keyword(s):  
Support Vector Machine ◽  
Small Error ◽  
Support Vector ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Training Samples ◽  
Testing Error ◽  
Machine Theory ◽  
Learning Machine ◽  
Limited Training Samples

Support vector machine is a beginning of the 1990s, based on statistical learning theory proposed new machine learning method, which structural risk minimization principle as the theoretical basis, by appropriately selecting a subset of functions and discriminant function in the subset, so the actual risk of learning machine to a minimum, to ensure that the limited training samples obtained through a small error classifier, an independent test set for testing error remains small. In this paper, support vector machine theory, algorithm, application status, etc. are discussed in detail.

Research on Fault Diagnosis Based on AGA and LSSVM

2011 ◽  
Vol 383-390 ◽  
pp. 6938-6941
Author(s):  
Da Wei Zhang ◽  
Kai Zhang ◽  
Jing Jiang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Empirical Risk Minimization ◽  
Risk Minimization ◽  
Empirical Risk ◽  
Parameters Selection ◽  
Structural Risk

Support vector machine (SVM) has excellent learning, classification ability and generalization ability, which uses structural risk minimization instead of traditional empirical risk minimization based on large sample. The perfect performance of SVM will be realized only if the parameters are rightly selected. The accuracy and efficiency of classification largely depend on the quality of the parameters selection. Focusing on the problem of the parameters selection in least squares support vector machine (LSSVM), a new method is proposed to optimize the parameters in LSSVM using adaptive genetic algorithm. The research is provided using this method on the fault diagnosis of a certain type of helicopter’s helicopter-electrical-box. Simulated results show that the proposed method achieves perfect accuracy and efficiency in fault diagnosis.

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