| 基于加速度时程的结构参数直接识别方法及验证 |
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| 引用本文： | 何定桥, 王鹏军, 杨军. 深度神经网络在EMD虚假分量识别中的应用[J]. 工程力学, 2021, 38(S): 195-201. DOI: 10.6052/j.issn.1000-4750.2020.04.S036 |
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| 作者姓名： | 何定桥 王鹏军 杨军 |
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| 作者单位： | 1.清华大学土木工程安全与耐久教育部重点实验室，北京 100084;2.清华大学电子工程系，北京 100084;3.北京源清慧虹信息科技有限公司，北京 100092 |
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| 基金项目： | 国家重点研发计划项目(2017YFC1500606-01) |
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| 摘 要： | 无线智能传感器结合云平台可以实现建筑结构的长期健康监测，模态识别是结构健康监测的重要内容。希尔伯特黄变换(Hilbert-Huang Transform, HHT)因其适用于非线性非平稳信号，且具有完全自适应性等特点，在模态识别领域中被广泛应用。与实验室中进行结构模态参数识别不同的是，长期监测中模态参数识别的算法不能出现主观的参数选择过程，而传统HHT的第一步经验模态分解(Empirical Mode Decomposition, EMD)会产生虚假的固有模态函数(Intrinsic Mode Function, IMF)分量，对虚假分量的识别与剔除往往依赖研究人员的主观判断。该文提出了一种基于深度神经网络(Deep Neural Networks, DNN)与K-L散度(Kullback-Leibler Divergence, K-L Divergence)的新算法，可以自动化识别并剔除EMD产生的虚假分量，从而使得EMD后得到的固有模态函数均为真实分量。
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| 关 键 词： | 结构健康监测 自动化模态参数识别 DNN HHT EMD K-L Divergence |
| 收稿时间： | 2020-04-28 |
| 修稿时间： | 2020-12-31 |
Structural parameters identification methodology with direct use of acceleration time series and its verification |
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| HE Ding-qiao, WANG Peng-jun, YANG Jun. APPLICATION OF DEEP NEURAL NETWORKS IN EMD FALSE COMPONENT IDENTIFICATION[J]. Engineering Mechanics, 2021, 38(S): 195-201. DOI: 10.6052/j.issn.1000-4750.2020.04.S036 |
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| Authors: | HE Ding-qiao WANG Peng-jun YANG Jun |
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| Affiliation: | 1.Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Tsinghua University, Beijing 100084, China;2.Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;3.Beijing Smartbow Technology Co., Ltd., Beijing 100092, China |
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| Abstract: | Wireless intelligent sensors combined with cloud storage technology can realize long-term health monitoring for structures. Modal identification is an important part for structural health monitoring. The Hilbert-Huang transform (HHT) is widely used for structural modal identification because it is suitable for nonlinear and non-stationary signals and is self-adaptable. The algorithm of modal identification in the long-term monitoring cannot rely on subjective parameter selection, while the first step of the traditional HHT may produce false intrinsic mode function (IMF) components. The identification and elimination of false IMF components often rely on subjective judgment. In this paper, a new algorithm based on deep neural networks (DNN) and Kullback-Leibler (K-L) divergence is proposed, which can automatically identify and eliminate the false components generated by empirical mode decomposition (EMD). |
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| Keywords: | structural health monitoring automatic modal parameters identification DNN HHT EMD K-L divergence |
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