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Abstract
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Article Information:
Structure Data Processing and Damage Identification Based on Wavelet and Artificial Neural Network
Zhanfeng Gao and Xiangjun Chen
Corresponding Author: Zhanfeng Gao
Submitted:
Accepted: 2011 September, 25
Published: 2011 October, 20 |
Abstract:
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Structural health monitoring is a multi-disciplinary integrated technology, mainly including signal
processing and structural damage detection. The aim of the data processing is to obtain the useful information
from large volumes of raw data containing noises. In order to obtain the useful information concerned, denoising
method and feature extraction technique based on Wavelet analysis is studied. An improved wavelet
thresholding algorithm to eliminate the noise for vibration signals is proposed. The results of analysis show that
the method based on Wavelet is not only feasible to signal de-noising, but also valuable and effective to detect
the health status of bridge structure. In order to detect the damage status of the structure, a multi-layer neural
network models based on the BP algorithm is designed. The model is trained with the data from an engineering
beam to filter different transfer function, train function and the unit number of hidden layer by contrast to
determine the best network model for damage detection. At last, the model is used to detect the damage of
cable-stayed bridge with an improved method of data pre-processing using the square rate of change in
frequency as input date of network. The structural damage identification results show that the BP neural
network model is easy to identify the damage by the changing of vibration modal frequency and effective to
reflect the injury status of the existing structure.
Key words: Artificial neural network, BP algorithm, damage identification, de-nosing, structure health monitoring, vibration signal, wavelet analysis
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Abstract
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Cite this Reference:
Zhanfeng Gao and Xiangjun Chen, . Structure Data Processing and Damage Identification Based on Wavelet and Artificial Neural Network. Research Journal of Applied Sciences, Engineering and Technology, (10): 1203-1208.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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