Title :
A degredation interval prediction method based on RBF neural network
Author :
Xiankun Zhang ; Fuqiang Sun ; Xiaoyang Li
Author_Institution :
Sci. & Technol. on Reliability & Environ. Eng. Lab., Beihang Univ., Beijing, China
Abstract :
In the area of reliability, remaining useful lifetime (RUL) prediction can help people establish reasonable maintenance strategies and then implement maintenance activities at a right time. In this paper, RBF neural network approach is applied in the degradation prediction process of a certain microwave component. A degradation model that describes how a certain degradation parameter changes over time is established and then the performance degradation trend can be obtained based on this model. And then a confidence interval prediction can be obtained based on traditional probability theory, which proves that the results have reached a high confidence level. Finally, the BP neural network approach is introduced as a comparison, and results indicate that the proposed method has higher precision and stability.
Keywords :
backpropagation; radial basis function networks; BP neural network approach; RBF neural network; RUL prediction; confidence interval prediction; degradation parameter; degradation prediction process; degredation interval prediction method; microwave component; probability theory; reasonable maintenance strategies; remaining useful lifetime; Biological neural networks; Data models; Degradation; Prediction algorithms; Predictive models; Training data; RBF neural network; RUL prediction; interval prediction; performance degradation;
Conference_Titel :
Reliability, Maintainability and Safety (ICRMS), 2014 International Conference on
Print_ISBN :
978-1-4799-6631-8
DOI :
10.1109/ICRMS.2014.7107194
