Similarity-Based Echo State Network for Remaining Useful Life Prediction

Abstract

The health state of aero engines will affect economic benefits, availability, and safety of aircrafts. Fault prognosis is considered to be one of useful methods to evaluate the health state of aero engines and avoid unscheduled down time. Prognosis is the ability to forecast the evolution of engine deterioration. The two major objectives of prognosis are: forecasting the impending failures and predicting the engines’ remaining useful life (RUL). This paper proposes a method that combines echo state network (ESN) with similarity-based methods for RUL prediction. First, principal component analysis (PCA) is used to pre-process data to obtain degradation trajectories, which handles the fusion problem of multiple data. Second, in order to use historical information accurately, the Euclidean distance is applied to calculate the similarity among different degradation trajectories. Third, the trajectory with the highest similarity is used in the ESN training process. Then, a numerical simulation on the NASA C-MAPSS dataset verifies the accuracy of the method. Finally, the comparison with other methods shows the superiority of the method.