A novel mixed uncertainty support vector machine method for structural reliability analysis

2021 ◽  
Vol 232 (4) ◽  
pp. 1497-1513
Author(s):  
Ling-Fei You ◽  
Jian-Guo Zhang ◽  
Shuang Zhou ◽  
Jie Wu
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Structural Reliability Analysis

Support vector machine for structural reliability analysis

2006 ◽  
Vol 27 (10) ◽  
pp. 1295-1303 ◽  
Author(s):  
Hong-shuang Li ◽  
Zhen-zhou Lü ◽  
Zhu-feng Yue
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Support Vector ◽  
Structural Reliability Analysis

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

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