| 姜绍飞,张帅.基于概率神经网络分类器的数据融合损伤检测方法[J].计算力学学报,2008,25(5): |
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| 基于概率神经网络分类器的数据融合损伤检测方法 |
| Data-fusion damage detection approach based on probabilistic neural network classifier |
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| DOI：10.7511/jslx20085132 |
| 中文关键词: 损伤检测 数据融合 小波能量特征 特征提取 概率神经网络 |
| 英文关键词:damage detection,data fusion,wavelet energy feature,feature extraction,probabilistic neural network |
| 基金项目:国家自然科学基金，辽宁高等学校优秀人才计划，福建高等学校新世纪优秀人才计划，福建省教育厅科研项目 |
| 姜绍飞 张帅 |
| 福州大学土木工程学院，沈阳建筑大学土木学院 |
| 摘要点击次数: 1646 |
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| 中文摘要: |
| 为了有效地利用来自大型结构健康监测系统大量冗余、互补的信息进行结构健康状况评估,本文从数据融合的基本原理入手,将小波分析、概率神经网络和数据融合等技术有机地结合起来,提出了一种5阶段的决策级数据融合损伤检测新方法,最后用2个数值算例验证了方法的有效性,并探讨了测量噪声对损伤识别的影响.研究结果表明,所提出的方法是可行、有效的. |
| 英文摘要: |
| In order to make full use of the redundant and complementary information and thus assess the structural health states from a large structural health monitoring system,the principle of data fusion was first introduced in this paper,then a 5-phase novel decision-level data fusion damage detection approach by integrating wavelet analysis,probabilistic neural network(PNN) and data fusion developed and implemented.Finally,two numerical examples validated the proposed method,the effect of measurement noise on identification accuracy was investigated as well.The result shows that the proposed method is feasible and effective for damage identification. |
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