Global-based structure damage detection using LVQ neural network and bispectrum analysis

被引：0
|
作者：
Dong, GM [1 ]
Chen, J
Lei, XY
Ning, ZG
Wang, DS
Wang, XX
机构：
[1] Shanghai Jiao Tong Univ, State Key Lab Vibrat Shock & Noise, Shanghai 200240, Peoples R China
[2] China Acad Engn Phys, Inst Struct Mech, Sichuan 621900, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS | 2005年 / 3498卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new approach for the global-based structure damage detection is proposed, which is based on the combination of bispectrum feature extraction technique and LVQ neural network identification method. A finite element model based on a steel frame structure with various joint damage patterns is analyzed. Results of analysis demonstrate higher damage identification capability in comparison with modal assurance criterion (MAC) method in a noise-contaminated environment.
引用
收藏
页码：531 / 537
页数：7
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