Fatigue crack prediction is a critical aspect of prognostics and health management research.The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fati...Fatigue crack prediction is a critical aspect of prognostics and health management research.The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fatigue growth,and it is widely used to predict the state of fatigue crack.This paper proposes a method of lamb wavebased early fatigue microcrack prediction with the aid of particle filters.With this method,which the changes in signal characteristics under different fatigue crack lengths are analyzed,and the state-and observation-equations of crack extension are established.Furthermore,an experiment is conducted to verify the feasibility of the proposed method.The Root Mean Square Error(RMSE)of the three different resampling methods are compared.The results show the system resampling method has the highest prediction accuracy.Furthermore,the factors affected by the accuracy of the prediction are discussed.展开更多
基金This work was supported by the National Natural Science Foundation of China(62073193,61903224,61873333)National Key Research and Development Project(2018YFE02013)Key research and development plan of Shandong Province(2019TSLH0301,2019GHZ004).
文摘Fatigue crack prediction is a critical aspect of prognostics and health management research.The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fatigue growth,and it is widely used to predict the state of fatigue crack.This paper proposes a method of lamb wavebased early fatigue microcrack prediction with the aid of particle filters.With this method,which the changes in signal characteristics under different fatigue crack lengths are analyzed,and the state-and observation-equations of crack extension are established.Furthermore,an experiment is conducted to verify the feasibility of the proposed method.The Root Mean Square Error(RMSE)of the three different resampling methods are compared.The results show the system resampling method has the highest prediction accuracy.Furthermore,the factors affected by the accuracy of the prediction are discussed.
