A degredation interval prediction method based on RBF neural network

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.

A Deep Adversarial Learning Prognostics Model for Remaining Useful Life Prediction of Rolling Bearing

“…For comparison purposes, six state-of-the-art RUL prediction methods are used in the experiments. For one-step time-series prediction, both conventional machine learning and deep learning methods, including SVR [18], radical basis function neural network (RBF) [40], and LSTM [33] are employed. For iterative multi-step ahead RUL prediction, the back-propagation neural network (BP) is used as a reference method.…”

Experiment and Modeling on Macro Fiber Composite Stress-Induced Actuation Function Degradation

“…The degradation of material performance is always accompanied by some form of material nonlinear mechanical behavior, hence the macroscopic appearance of degradation will also be nonlinear. The radial basis function (RBF) neural network model can approach any non-linear functions [16], and this feature is used for modeling the degradation paths based on known degradation data. In order to obtain a prediction model for the actuation function degenerate degree prediction of the MFC actuator undergoing cyclic load, the RBF neural network learning algorithm is adopted.…”