Multi-class Fuzzy Support Vector Machine Research Articles

Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine

This paper presents an approach for fault diagnosis of wind turbine (WT) based on multi-class fuzzy support vector machine (FSVM) classifier. In this method, empirical mode decomposition is adopted to extract fault feature vectors from vibration signals. FSVM is used for solving classification problem with outliers or noises, where kernel fuzzy c-means clustering algorithm and particle swarm optimization algorithm are applied to calculate fuzzy membership and optimize the parameters of kernel function of FSVM, respectively. In addition, to study the performance of the proposed approach, another two widely used methods, named back propagation neural network and standard support vector machine, are studied and compared. Discrete wavelet transform is also used to extract fault feature vectors. To validate the proposed approach, a direct-drive WT test rig is constructed and the experiments are carried out. The experimental results show that the proposed approach is an effective fault diagnosis method for WT, which has a better performance and can achieve higher diagnostic accuracy.

Fault diagnosis model based on fuzzy support vector machine combined with weighted fuzzy clustering

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC). Considering the relationship between the sample point and non-self class, FC algorithm is applied to generate fuzzy memberships. In the algorithm, sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC. Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm, the penalty factor and kernel parameter of which are optimized by GA. Finally, the model is executed for multi-class fault diagnosis of rolling element bearings. The results show that the presented model achieves high performances both in identifying fault types and fault degrees. The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM

On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

Water Quality Evaluation Based on Multiclass Fuzzy Support Vector Machine

It has been a more complex problem for water quality assessment. And its aim is to well and truly evaluate its degree of pollution for bodies of water, which will be easy to provide some principled projects and criterions for water resource’s protection and their integration application. So, a water quality assessment method based on Multiclass Fuzzy Support Vector Machine is put forward. and a two-step cross-validation was used to search for the best combination of parameters to obtain an optimal training model. The test results show that the method proposed in this paper has an excellent performance on correct ratio compared to BP. It indicated that the performance of the proposed model is practically feasible in the application of water quality assessment.

Multi-class support vector machine for classification of the ultrasonic images of supraspinatus

This article proposes an effort to apply the multi-class support vector machine classifiers to classify the supraspinatus image into different disease groups that are normal, tendon inflammation, calcific tendonitis and supraspinatus tear. The supraspinatus tendon is often involved in the above-mentioned disease groups. Four different texture analysis methods – texture feature coding method, gray-level co-occurrence matrix, fractal dimension evaluation and texture spectrum – are used to extract features of tissue characteristic in the ultrasonic supraspinatus images. The mutual information criterion is adopted to select the powerful features from ones generated from the above-mentioned four texture analysis methods in the training stage, meanwhile, the five implementations of multi-class support vector machine classifiers are also designed to discriminate each image into one of the four disease groups in the classification stage. In experiments, the most commonly used performance measures including sensitivity, specificity, classification accuracy and false-negative rate are applied to evaluate the classification of the five implantations of multi-class support vector machines. In addition, the receiver operating characteristics analysis is also used to analyze the classification capability. The present results demonstrate that the implementation of multi-class fuzzy support vector machine can achieve 90% classification accuracy, and performance measures of this implementation are significantly superior to the others.