STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORK

Abstract

Adaptive time frequency analysis based on local mean decomposition and nonlinear quantization ability of sample entropy,combined with radial basis function（ RBF） neural network. A method of structural damage identification based on local mean decomposition（ LMD） sample entropy and radial basis function neural network is proposed. Firstly,the original signal is decomposed into a number of product function components（ PF component） by LMD to the original signal of structure vibration.Then extract the sample entropy of the first 3 PF components to realize the feature quantization of the PF component. Finally,the sample entropy of the component is used as the damage characteristic vector. The radial basis function neural network is used to identify the bottom plate of scaled carbody for high-speed train. The experimental results show that while this method is used to identify structural damage,the damage identification errors of location and degree are 96. 97% and 96. 25% respectively. The validity and accuracy of this method in structural damage diagnosis are proved.

Keywords

• Damage identification;Local mean decomposition;Sample entropy;Radial basis function neural network