A Similarity-Based Remaining Useful Life Prediction Method for Aero Engines with Small Smples

Abstract

As the power system of an aircraft, accurate prediction of the remaining useful life (RUL) of an aero-engine is of great importance to ensure the flight safety of the aircraft. However, existing methods are all data-driven-based, and such methods are extremely demanding in terms of data volume. To address the problem of insufficient engine data, this paper proposes a similarity-based method for predicting the life of small-sample aircraft engines. Firstly, the KPCA method is used to model the engine degradation trajectory, then a simple and effective method is proposed to determine the degradation start moment of each engine, and finally the similarity between each training sample and the test sample is determined based on the trained KPCA model, and then the remaining life of the test sample is estimated. Experiments show that the method proposed in this paper is effective in predicting the remaining life of an engine under the condition of small samples.