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- W2379486072 abstract "According to the fact that in fault diagnosis for internal leakage of hydraulic cylinder,excessive time-domain features are selected from pressure signal and the features interact,a fault diagnosis approach based on principal compo-nent analysis(PCA) and improved Levenberg-Marquardt(LM) neural network was proposed.LU decomposition was em-ployed to improve the solution of contrary matrix in the LM algorithm simulated with MATLAB at first in order to increase the convergence speed of the network.And then eight time-domain parameters of pressure signal were selected as prime features,PCA was used to reduce the dimension and eliminate the correlativity,and the first two principal components were selected as the final features.At last,these final features were input into the improved LM neural network to identify faults.The simulation and comparison results of the proposed method,LM algorithm and GA-BP algorithm reveal that the proposed method can meet the recognition rate and increase diagnosis speed,so it is an effective fault diagnosis approach for internal leakage of hydraulic cylinder." @default.
- W2379486072 created "2016-06-24" @default.
- W2379486072 creator A5005855025 @default.
- W2379486072 date "2013-01-01" @default.
- W2379486072 modified "2023-09-28" @default.
- W2379486072 title "Internal leakage fault diagnosis approach of hydraulic cylinder using LMBP neural network" @default.
- W2379486072 hasPublicationYear "2013" @default.
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