| Abstract:Aiming at the problem that the common feature selection methods are difficult to measure the nonlinear relationship
between sensors and performance degradation, and cannot accurately screen sensors to construct health factors, resulting in high life predic?
tion errors, a similarity engine life prediction method based on Copula entropy for sensor selection was proposed. Firstly, sensor degradation
characteristics with the influence of operating conditions eliminated were obtained based on K-Means clustering. Secondly, the nonlinear
index-Copula entropy was used to select the optimal sensor closely related to the original health factor and reconstruct the health factor.
Then, exponential degradation models were established for each group of failed engines, and the health factors of the current operating
cycle of the in-service engines were predicted according to these models. The similarity distance between the two engines was defined
based on the actual and predicted value of the health factors. Search the equipment similar to the in-service engine in the database of failed
engines and predict the remaining useful life of the engine . Finally, the feasibility and validity of the method were verified based on the C-
MAPSS dataset. The results show that the prediction errors of the proposed method are reduced by 39.25%, 41.69%, and 50.53%
respectively at 50%, 70% and 90% of the operating cycle, effectively improving the accuracy of life prediction. |
