Abstract
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.
Original language | English (US) |
---|---|
Journal | Advances in Mechanical Engineering |
Volume | 9 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2017 |
Externally published | Yes |
Keywords
- Bayesian least-squares support vector machine
- Remaining useful life
- confidence bands
- microwave component
ASJC Scopus subject areas
- Mechanical Engineering