The transmission and storage of the measured data were excessive in the present structural health monitoring system because of the overloaded signal.Thus

it is proposed to apply compression sensing method to the neural network system and electro-mechanical impedance（EMI）technology for structural damage identification.The compressed EMI data was used to depict the structural damages instead of the original ones.The discrete cosine transform（DCT）was employed to analyze the sparseness degree of the EMI signal.Furthermore

to satisfy the restricted isometry conditions of the compression perception theory

the Gauss random matrix was used as the observation matrix.The finite element EMI model was first established for a gear structure and the conductance data were accordingly obtained for various crack depths.The data compressed by the compression perception method was investigated using the principal component analysis and the obtained principal components were then used to be the input parameters of the BP neural network.Results show that the transmission bandwidth and storage space of the data are only 44% of the original ones after using the compression perception theory

and the neural network system could detect the existence of cracks and could further classify the crack severities quantitatively using the principal components of the compressed conductance.