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

作者：Sun, Fuqiang; Li, Xiaoyang; Liao, Haitao^*; Zhang, Xiankun

来源：Advances in Mechanical Engineering, 2017, 9(1): 1687814016685963.

DOI：10.1177/1687814016685963

• Remaining useful life
• Bayesian least-squares support vector machine
• confidence bands
• microwave component

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.

• 出版日期2017-1
• 单位北京航空航天大学