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2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Xu, Yunjie Xu, Yunjie Li, Wenbin Beijing Forestry Univ Engn Coll Beijing 100083 Peoples R China Zhe Jiang Forestry Univ Engn Coll Lin An 311300 Peoples R China

ISBN: (纸本)9781424455690

Failure of gearbox is very complex, so it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on genetic-support vector machine (GSVM) approach is presented for fault diagnosis of gearbox. The performance of the GSVM system proposed in this study is evaluated by gearbox in the wood-wool working device. The test results show that this GSVM model is effective to detect failure of gearbox in the wood-wool working device.

关键词： kernel function parameter genetic-support vector machine fault diagnosis wood-wool working device

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Research on multi-class classification of Support Vector Data Description

Research on multi-class classification of Support Vector Dat...

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Conference on Pattern Recognition and Computer Vision

作者： Shen, Minghua Xiao, Huaitie Fu, Qiang Natl Univ Def & Technol Sch Elect Sci & Engn Changsha 410073 Peoples R China

ISBN: (纸本)9780819469526

Support Vector Data Description (SVDD) is a one-class classification method developed in recent years. It has been used in many fields because of its good performance and high executive efficiency when there are only one-class training samples. It has been proven that SVDD has less support vector numbers, less optimization time and faster testing speed than those of two-class classifier such as SVM. At present, researches and acquirable literatures about SVDD multi-class classification are little, which restricts the SVDD application. One SVDD multi-class classification algorithm is proposed in the paper. Based on minimum distance classification rule, the misclassification in multi-class classification is well solved and by applying the threshold strategy the rejection in multi-class classification is greatly alleviated. Finally, by classifying range profiles of three targets, the effect of kernel function parameter and SNR on the proposed algorithm is investigated and the effectiveness of the algorithm is testified by quantities of experiments.

关键词： Support Vector Data Description kernel function parameter pattern recognition multi-class classification

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Hybrid Approach to Improving the Results of the SVM Classification Using the Random Forest Algorithm 13

Hybrid Approach to Improving the Results of the SVM Classifi...

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13th International Symposium on Intelligent Systems (INTELS)

作者： Demidova, L. A. Klyueva, I. A. Pylkin, A. N. Ryazan State Radio Engn Univ 59-1 Gagarina Str Ryazan 390005 Russia

The aim of this work is to improve the results of the SVM classification (Support Vector Machine) by hybridizing the SVM classifier with the random forest classifier (Random Forest, RF) used as the auxiliary. Specification of the classification decisions obtained on the basis of the SVM classifier is performed for the objects located in the experimentally determined subareas near the hyperplane separating the classes and including both correctly and erroneously classified objects. In the case of improving the quality of the objects classification from the initial dataset, the proposed hybrid approach to the objects classification can be recommended for classification of new objects. When developing the SVM classifier, the fixed default parameters values are used. A comparative analysis of the classification results obtained during the computational experiments in the hybridization of the SVM classifier with two auxiliary classifiers the random forest classifier (RF classifier) and the k nearest neighbor classifier (kNN classifier), for which the parameters values are determined randomly, confirms the expediency of using of these classifiers to increase the SVM classification quality. It was found that in most cases, the random forest classifier works better in terms of improving the SVM classification quality in comparison with the kNN classifier. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： intelligent classification algorithm SVM algorithm k-nearest neighbors algorithm random forest algorithm support vector kernel function kernel function parameter regularization parameter hybridization

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Fault Diagnosis Method for Power Transformer Based on Ant Colony - SVM Classifier

Fault Diagnosis Method for Power Transformer Based on Ant Co...

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2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Niu Wu Xu Liangfa Hu Sanguo First Aeronaut Inst Air Force Dept Fdn Xinyang 464000 Peoples R China First Aeronaut Inst Air Force Dept 3 Xinyang 464000 Peoples R China

ISBN: (纸本)9781424455690

Failure of power transformer is very complex, so that it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on ant colony-support vector machine (AC-SVM) approach is presented for fault diagnosis of power transformer. The AC-SVM selects kernel function parameter and soft margin constant C penalty parameter of support vector machine (SVM) classifier. The performance of the AC-SVM system proposed in this study is evaluated by cases in China. The test results show that this AC-SVM model is effective to detect failure of power transformer.

关键词： kernel function parameter ant colony-support vector machine fault diagnosis transformer

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Study of Fault Feature Extraction Based on KPCA Optimized by PSO Algorithm

Study of Fault Feature Extraction Based on KPCA Optimized by...

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2010 IEEE World Congress on Computational Intelligence

作者： Pan Hongxia Wei Xiuye Huang Jinying North Univ China Sch Mech Engn & Automat Taiyuan 030051 CO Peoples R China North University China Sch Mech Engn Taiyuan 030051 CO Peoples R China

ISBN: (纸本)9781424469208

For blindness of the parameter settings in kernel principal component analysis (KPCA), kernel function parameter optimized by particle swarm optimization (PSO) algorithm is proposed, and KPCA is applied to feature extraction in fault diagnosis. The mathematical model of kernel function parameter optimized is constructed firstly, then the PSO algorithm with adaptive accelerate (CPSO) is used to optimize it. The optimized KPCA is applied to feature extraction of gearbox typical faults. The results indicate that KPCA after parameter optimized can effectively reduce the dimensions of feature vector of gearbox, and it has a better fault classification performance than linear principal component analysis (PCA). This method has an advantage in nonlinear feature extraction of mechanical failure signal.

关键词： KPCA PSO algorithm condition monitoring fault classification fault diagnosis fault diagnosis fault feature extraction feature extraction feature vector dimension gearbox gears kernel function parameter kernel principal component analysis linear principal component analysis mathematical model mechanical engineering computing mechanical failure signal nonlinear feature extraction particle swarm optimisation principal component analysis signal processing

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Hybrid Approach to Improving the Results of the SVM Classification Using the Random Forest Algorithm

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Procedia Computer Science 2019年 150卷 455-461页

作者： L.A. Demidova I.A. Klyueva A.N. Pylkin Ryazan State Radio Engineering University 59/1 Gagarina Str. Ryazan 390005 Russia

The aim of this work is to improve the results of the SVM classification (Support Vector Machine) by hybridizing the SVM classifier with the random forest classifier (Random Forest, RF) used as the auxiliary. Specification of the classification decisions obtained on the basis of the SVM classifier is performed for the objects located in the experimentally determined subareas near the hyperplane separating the classes and including both correctly and erroneously classified objects. In the case of improving the quality of the objects classification from the initial dataset, the proposed hybrid approach to the objects classification can be recommended for classification of new objects. When developing the SVM classifier, the fixed default parameters values are used. A comparative analysis of the classification results obtained during the computational experiments in the hybridization of the SVM classifier with two auxiliary classifiers – the random forest classifier (RF classifier) and the k nearest neighbor classifier ( k NN classifier), for which the parameters values are determined randomly, confirms the expediency of using of these classifiers to increase the SVM classification quality. It was found that in most cases, the random forest classifier works better in terms of improving the SVM classification quality in comparison with the k NN classifier.

关键词： intelligent classification algorithm SVM algorithm k -nearest neighbors algorithm random forest algorithm support vector kernel function kernel function parameter regularization parameter hybridization

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Application of Particle Swarm Optimization to Fault Condition Recognition Based on kernel Principal Component Analysis

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International Journal of Plant Engineering and Management 2009年第3期000卷

作者： WEI Xiu-ye PAN Hong-xia HUANG Jin-ying WANG Fu-jie College of Mechanical Engineering and Automation North University of ChinaTaiyuan 030051P.R.China

Panicle swarm optimization (PSO) is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis (KPCA) for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration (CPSO) is applied to optimizing it. It is used for gearbox condition recognition, and the result is compared with the recognized results based on principal component analysis (PCA).The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA bused on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.

关键词： particle swarm optimization kernel principal component analysis kernel function parameter feature extraction gearbox condition recognition

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