NDLI: Study on the damage identification of large power transmission tower based on wavelet packet energy and neural network

Content Provider	IEEE Xplore Digital Library
Author	Liu Chun-cheng Zhang Wei Sun Xian-he Chu Zheng-yu
Copyright Year	2010
Description	Author affiliation: School of Civil and Architecture Engineering, Northeast Dianli University, Jilin, China (Zhang Wei; Sun Xian-he; Chu Zheng-yu) \|\| School of Civil and Architecture Engineering, Northeast Dianli University and State Key Lab of Coastal and Offshore Engineering, Dalian University of Technology, Jilin, China (Liu Chun-cheng)
Abstract	Based on wavelet packet energy and RBF neural network, the damage identification of principle material of a long span power transmission tower is presented in this paper. The dynamic response signal of displacement-time of the damage and undamaged tower subjected to seismic excitation is obtained. In addition, the wavelet packet energy curvature difference of pre-damage and post-damage states of tower by decomposing and reconstructing signal is given. By selecting the first to the sixth component of wavelet packet energy, the single and multiple damage locations are identified accurately. Moreover, the RBF neural network is applied on damage extent identification of the tower. The results show that the predicted values are in good agreement with the target values, so that RBF neural network can effectively identify the damage extent. From the analysis of damage sensitivity, it can be found that the damage location may still be accurately identified in addition of 10% and 20% random white noise. Consequently, the proposed method of wavelet packet energy has some resistance capacity to noise.
File Size	311181
File Format	PDF
ISBN	9781424468928
e-ISBN	9781424468935
DOI	10.1109/ICSPS.2010.5555812
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	energy curvature difference Poles and towers Noise Artificial neural networks Wavelet packet Wavelet analysis Wavelet packets Finite element methods transmission tower damage identification neural network
Content Type	Text
Resource Type	Article

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