A Method of State Diagnosis for Rolling Bearing Using Support Vector Machine and BP Neural Network

It is not easy to find marine cracks of structures by directly manual testing. When the cracks of important components are extended under extreme offshore environment, the whole structure would lose efficacy, endanger the staff’s safety, and course a significant economic loss and marine environment pollution. Thus, early discovery of structure cracks is very important. In this paper, a beam structure damage identification model based on intelligent algorithm is firstly proposed to identify partial cracks in supported beams on ocean platform. In order to obtain the replacement mode and strain mode of the beams, the paper takes simple supported beam with single crack and double cracks as an example. The results show that the difference curves of strain mode change drastically only on the injured part and different degrees of injury would result in different mutation degrees of difference curve more or less. While the model based on support vector machine (SVM) and BP neural network can identify cracks of supported beam intelligently, the methods can discern injured degrees of sound condition, single crack, and double cracks. Furthermore, the two methods are compared. The results show that the two methods presented in the paper have a preferable identification precision and adaptation. And damage identification based on support vector machine (SVM) has smaller error results.

Download Full-text

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.666.203 ◽

2014 ◽

Vol 666 ◽

pp. 203-207

Author(s):

Jian Hua Cao

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Control System ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Experimental Results ◽

Support Vector ◽

Electrical Control ◽

Diagnosis Method ◽

The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

Download Full-text

Application of Support Vector Machine in Springback Forecasting for Steel Structure

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.166-169.1366 ◽

2012 ◽

Vol 166-169 ◽

pp. 1366-1369

Author(s):

Jian Guo Chen ◽

Zhao Guang Li

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Friction Coefficient ◽

Bp Neural Network ◽

Prediction Error ◽

Steel Structure ◽

Support Vector ◽

Springback Prediction ◽

Better Than

Support vector machine is applied to springback forecasting for steel structure in the paper. In the steel structure, pressure-pad-force, friction coefficient and die filleted corner have a certain influence on springback amount.We employ BP neural network to compare with support vector machine to show the superiority of support vector machine in this study. Finally,we give the comparison of the prediction error of springback for steel structure between support vector machine and BP neural network. Evidently,the springback prediction for steel structure of support vector machine is better than that of BP neural network.

Download Full-text

Speech Recognization Based on Support Vector Machine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.7516 ◽

2012 ◽

Vol 433-440 ◽

pp. 7516-7521

Author(s):

Ling Zhang

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Speech Recognition ◽

Bp Neural Network ◽

Speech Signal ◽

Local Minimum ◽

Support Vector ◽

Neural Network Method ◽

Network Method ◽

Fast Classification

Aiming at the deficiency of the local minimum occurring in neural network used for speech recognition, the paper employs support vector machine (SVM) to recognize the speech signal with four different components. First, SVM is utilized to perform the speech recognition. Then, the results are compared with those obtained by the BP neural network method. The comparison shows that SVM effectively overcomes the local minimum existing in neural network and has the advantages of the accurate and fast classification, indicating that SVM looks feasible to recognize the speech signal.

Download Full-text

Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

IEEE Access ◽

10.1109/access.2020.3012053 ◽

2020 ◽

Vol 8 ◽

pp. 137395-137406 ◽

Cited By ~ 2

Author(s):

Laohu Yuan ◽

Dongshan Lian ◽

Xue Kang ◽

Yuanqiang Chen ◽

Kejia Zhai

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Download Full-text

Steel Pip Corrosion Forecasting Based on Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.166-169.1002 ◽

2012 ◽

Vol 166-169 ◽

pp. 1002-1006

Author(s):

Guang Yue Ma

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Bp Neural Network ◽

Learning Algorithm ◽

Support Vector ◽

Risk Minimization ◽

Prediction Ability ◽

Structural Risk ◽

Better Than ◽

Local Extreme

BP neural network has some shorcomings,such as local extreme. Support vector machine is a novel statistical learning algorithm,which is based on the principle of structural risk minimization. In the paper, support vector machine is used to perform steel pip corrosion forecasting.The collected steel pip corrosion forecasting experimental data are given,among which corrosion deeps from 8ths to 11ths are used to test the proposed prediction model. BP neural network is applied to steel pip corrosion deep forecasting,which is used to compare with support vector machine to show the superiority of support vector machine in steel pip corrosion forecasting.The comparison of the prediction error of steel pip corrosion deep between support vector machine and BP neural network is given. It can be seen that the prediction ability for steel pip corrosion deep of support vector machine is better than that of BP neural network

Download Full-text

Prediction of Machine Tool Thermal Error Compensation Based on SVMR and ARM11

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.740.120 ◽

2015 ◽

Vol 740 ◽

pp. 120-126

Author(s):

Zhi Peng Zhang ◽

Kang Liu ◽

Feng Guo

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Machine Tool ◽

Bp Neural Network ◽

Error Compensation ◽

Thermal Error ◽

Support Vector ◽

Experiment Research ◽

Further Development ◽

Thermal Error Compensation

In order to improve the process precision of the machine tool, further development of SVMR was achieved by QT Creator. Support vector machine was applied to the ARM11 development board, SVMR model was online trained and real-time predicted the values of machine tool thermal error. Compared with the widely used BP neural network, this method has the characteristics of high compensation precision and strong generalization ability. Experiment research has proved that the stronger effectiveness and higher accuracy using this method.

Download Full-text

Research of GPS Elevation Conversion Based on Least Square Support Vector Machine and BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.501-504.2166 ◽

2014 ◽

Vol 501-504 ◽

pp. 2166-2171 ◽

Cited By ~ 2

Author(s):

Li Long Liu ◽

Teng Xu Zhang ◽

Miao Zhou ◽

Wei Wang ◽

Liang Ke Huang

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Bp Neural Network ◽

Curve Surface ◽

Least Square ◽

Support Vector ◽

Combined Model ◽

Polynomial Curve ◽

Optimal Weighting ◽

Bp Neural Network Model

This paper proposed the optical weighting combined mode of Least Square Support Vector Machine (LS-SVM) and BP Neural network. According to the measured data, this paper compared and analyzed the accuracy of LS-SVM model, BP Neural network model; quadratic polynomial curve surface fitting based on Total least-square algorithm and optimal weighting combined model, the data shows that the optimal weighting combined model has higher precision then others.

Download Full-text