ROBUST AUTONOMOUS DAMAGE DETECTION AND ASSESSMENT IN POLYMERIC COMPOSITE STRUCTURES

被引:0
作者
Kesavan, Ajay [1 ]
John, Sabu [1 ]
Li, Henry [1 ]
Herszberg, Israel
机构
[1] RMIT Univ, Sch Aerosp & Mech Engn, Bundoora, Vic 3083, Australia
来源
SMASIS 2008: PROCEEDINGS OF THE ASME CONFERENCE ON SMART MATERIALS, ADAPTIVE STRUCTURES AND INTELLIGENT SYSTEMS - 2008, VOL 2 | 2009年
关键词
JOINT;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper introduces the some of the experimental and analytical work behind the autonomous damage detection technique. The research study conducted here resulted in the development of a Structural Health Monitoring (SHM) system for a 2-D polymeric composite T-Joint, used in maritime structures. Two methods of damage detection are discussed - A statistics-based outlier technique and one using Artificial Neural Networks (ANNs). The SHM using ANNs system was found to be capable of not only detecting the presence of multiple delaminations in a composite structure, but also capable of determining the location and extent of all the delaminations present in the T-Joint structure, regardless of the load (angle and magnitude) acting on the structure. The system developed relies on the examination of the strain distribution of the structure under operational loading. Finally, on testing the SHM system developed with strain signatures of composite T-joint structures, subjected to variable loading, embedded with all possible damage configurations (including multiple damage scenarios), an overall damage (location & extent) prediction accuracy of 94.1% was achieved. These results are presented and discussed in detail in this paper.
引用
收藏
页码:67 / 78
页数:12
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