.png){kind=icon}
返回
Wavelet Network Approach for Structural Damage Identification Using Guided Ultrasonic Waves
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2014.2299528.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
An appropriate wavelet network (WN) approach is introduced for detecting damage location and severity of structures based on measured guided ultrasonic wave (GUW) signals. An algorithm for establishing a multiple-input multiple-output fixed grid wavelet network (FGWN) is proposed. This algorithm consists of three main stages: 1) formation of wavelet latticel; 2) formation of wavelet matrix; and 3) optimizing the wavelet structure by means of orthogonal least square algorithm. Three damage-sensitive features are extracted from the GUW signals: 1) time of flight; 2) normalized damage wave amplitude; and 3) normalized damage wave area. These features are considered as the FGWN inputs and the damage location and severity are estimated. The established FGWN is used for identifying damage location and severity in a structural beam. The beam is investigated and simulated in different damaged conditions. Computed finite element method (FEM) simulation signals are used for training the FGWN. Some other FEM simulation signals, as well as measured experimental ones are used for testing. The proposed damage identification method is compared with three artificial neural network (ANN)-based algorithms. In addition to some other benefits of the proposed WN-based algorithm over ANN-based methods discussed in this paper, the results show that our approach performs better in both damage location and severity detections than other methods.
Keyword:
Artificial neural network (ANN)
damage identification
guided ultrasonic wave (GUW)
structural health monitoring (SHM)
wavelet network (WN)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
.png){kind=icon}
Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine
PREAI
分享收藏分享收藏
查看更多内容