Prognostics of high frequency receiver based on evidential regression

Uncertainty management has always been the key hurdle faced by Data-driven prognostics. To solve this problem, a remaining useful life (RUL) estimation method based on evidential regression algorithm is proposed. The evidential regression method regards the k nearest neighbors as k pieces of evidence, whose beliefs are assigned to be proportional to their similarity to the features under prognostics. Then all the beliefs are pooled using the Dempster-Shafer theory. Finally, the estimation of RUL and the corresponding bounds are obtained by assignment of the uncertain belief. This method is applied to the prognostics of high frequency receiver, and the results show that this method has a better performance and is less sensitive to the uncertainty in the prognostics.