| 基于潜在成分和概率神经网络的时变结构系统的损伤识别 |
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| 引用本文： | 袁健,周燕. 基于潜在成分和概率神经网络的时变结构系统的损伤识别[J]. 南京航空航天大学学报, 2009, 41(3) |
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| 作者姓名： | 袁健 周燕 |
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| 作者单位： | 南京航空航天大学航空宇航学院,南京,210016;南京交通职业技术学院汽车工程系,南京,211188 |
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| 摘 要： | 介绍了基于潜在成分(LC)分析和概率神经网络的损伤识别方法,并应用于一个实验室模型的损伤识别.结果表明,基于潜在成分(LC)分析和概率神经网络的损伤识别方法能在正常的时变质量情况下以较高的成功率对位于A或B处的某一损伤程度未知的损伤进行归类,为时变结构系统的定量损伤识别作出了有益的尝试.
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| 关 键 词： | 时变结构 结构健康监测 损伤识别 潜在成分(LC) 概率神经网络 |
Damage Identification by Probabilistic Neural Networks Based on Latent Components for Time-Varying Structure System |
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| Yuan Jian,Zhou Yan. Damage Identification by Probabilistic Neural Networks Based on Latent Components for Time-Varying Structure System[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2009, 41(3) |
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| Authors: | Yuan Jian Zhou Yan |
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| Affiliation: | 1.College of Aerospace Engineering;Nanjing University of Aeronautics & Astronautics;Nanjing;210016;China;2.Department of Automobile Engineering;Nanjing Institute of Communications;211188;China |
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| Abstract: | A novel method of damage identification for health monitoring of a time-varying system is presented.The functional-series time-dependant automation auto regressive moving average(FS-TARMA) time series model is applied to vibration signal observed in time-varying system for estimating TAR/TMA parameters and innovation variance.They are the time function represented by the group of projection coefficients on certain functional subspace with specific basis functions.The estimated TAR/TMA parameters and innovat... |
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| Keywords: | time-varying structure structure health monitoring damage identification latent components probabilistic neural networks |
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