Reliability Analysis for Link Mechanism under Influence of Multiple Factors Based on Support Vector Machine

2013 ◽  
Vol 753-755 ◽  
pp. 2904-2907 ◽  
Author(s):  
Xue Wei Ke ◽  
Jian Hou ◽  
Ting Feng Chen
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
Support Vector ◽  
Computational Costs ◽  
Link Mechanism ◽  
Normal Probability ◽  
Normal Probability Distribution ◽  
Dimensional Errors ◽  
Multi Body ◽  
External Loads

Considering the influence of dimensional errors,clearances,friction coefficients,external loads and flex of part comprehensively,a multi-body dynamic model of link mechanism is established by using commercial software.Assuming that the above factors follow normal probability distribution are independent with each other,a mechanism reliability analysis method by combining simulation technology and support vector machine (SVM) are proposed to reduce the computational costs. The obtained results show that the computational costs of the proposed methods are much less than the computational costs of Monte Carlo Simulation (MCS).Therefore, the proposed methods might be efficient and valuable for the reliability analysis of complex mechanism.

Application of SVM to Reliability Analysis of Foundation Excavations System

2013 ◽  
Vol 859 ◽  
pp. 315-321 ◽  
Author(s):  
Jing Cao ◽  
Chang Ning Sun ◽  
Hai Ming Liu
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
Response Surface ◽  
Correlation Coefficient ◽  
Failure Modes ◽  
Pearson Correlation ◽  
Support Vector ◽  
Random Parameters ◽  
Pearson Correlation Analysis ◽  
Instance Analysis

The correlation of failure modes needs to be considered in the reliability analysis of foundation excavations system. Because it is difficult to calculate the correlation coefficient of failure modes, the computational efficiency of traditional method is low. In this paper, the response surface (RS) is established by using the uniform test and support vector machine (SVM). On this basis, in order to obtain the index of each failure mode, the random parameters generated by Monte Carlo simulation are predicted. Combined with the Pearson correlation analysis, the correlation coefficient of failure modes is obtained. And then, the Breadth Border Method, Narrow Bounds Method and PNET method are used to calculate system failure probability of foundation excavations. The reliability analysis method of the foundation excavations system based on the response surface of the support vector machine (RSSVM) is put forward. The instance analysis shows that the method is simple in calculation, and provides a convenient way for the system reliability theory of foundation excavations.

Reliability analysis of tunnel using least square support vector machine

2014 ◽  
Vol 41 ◽  
pp. 14-23 ◽  
Author(s):  
Hongbo Zhao ◽  
Zhongliang Ru ◽  
Xu Chang ◽  
Shunde Yin ◽  
Shaojun Li
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
Least Square ◽  
Support Vector

Reliability Analysis of Foundation Pit Based on Response Surface of Support Vector Machine

2013 ◽  
Vol 671-674 ◽  
pp. 240-244
Author(s):  
Chang Ning Sun ◽  
Jing Cao ◽  
Hai Ming Liu ◽  
Hui Min Zhao
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
Response Surface ◽  
Safety Factor ◽  
Lateral Displacement ◽  
Support Vector ◽  
Probability Density Distribution ◽  
Analysis Method ◽  
Foundation Pit ◽  
Random Samples

Traditional analysis methods of reliability in the foundation pit engineering have larger error and larger amount of calculation. Therefore, the response surface method has attracted much attention because it can effectively use the finite element analysis method (FEAM) and reduce the number of the numerical simulation. This paper combines uniform design (UD) with support vector machine (SVM). On this base, a reliability analysis method of the foundation pit is put forward based on the response surface of support vector machine (RSSVM). The UD structures random samples and the FEAM is used to obtain corresponding response parameters including the lateral displacement of wall, settlement of ground, safety factor of overall stability and safety factor of against overturning. Then, SVM trains the above random samples and corresponding response parameters to get response surface (RS) respectively. The probability density distribution of each response parameter is obtained by combining the Monte Carlo method with RSSVM. The instance analysis shows that the method has high computing efficiency and less amount of calculation, and the result is reasonable. It provides an effective way for the reliability analysis of the foundation pit engineering.

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.

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