Intrinsic Mode Decomposition Method for Damage and System Identification of In-Situ Civil Infrastructures

原位民用基础设施损伤本征模态分解方法及系统辨识

• 批准号: 9807855
• 负责人: Jann Yang
• 金额: $ 28.7万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-10-15 至 2002-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9807855&HistoricalAwards=false
• 关键词: Intrinsic; Mode; Decomposition; Method; Damage

9807855YangTradtional data Analysis techniques have been dominaed by the Fourier transform. The main restrictions are that the data should be stationary and long enought, and that the system generating the data must be linear. Unfortunately, when the structure is damaged, such as cracking or yielding, the response process in nonlinear, and consequently, Fourier or Fourier-based transform methods such as wavelet analysis approach, is not capable of capturing the intrinsic nonlinear and nonstationary characteristics of the physical process involved in the vibration data. A new technique, the Empirical Mode decompositon (EMD) method and Hlbert spectral analysis, recently developed at NASA Goodard space Flight Center, has been demonstrated to offer a great potential for nonstationary and nonlinear data analyses for wide engineering applications. This project is a collaborative research between the University of California, Irvine, and NASA Goddard space Flight Center. It is aimed at further development of the recently proposed EMD method and hilbert spectral analysis for applications to damage detection and system identification of civil infrastructures. In addition, efforts will be made to separate the polluted noises from measured vibration data and to capture the intrinsic linear or nonlinear characteristics of the structural response. Signatures resulting from the energy release due to structural damage will also be identified and used to characterize the extent of structural damage. The unique approach proposed is based on the innovative and powerful data analysis technique to capture the intrinsic nonlinear and nonstatinary characteristics of the measured vibratin data from which system identification and damagedetection can be made. Teh results of this research project are expected to make a significant advancement of the state-of-the technology in system identification and damage detection of civil infrastructures.

传统的数据分析技术一直被傅里叶变换所主导。主要的限制是数据应该是固定的并且足够长，并且生成数据的系统必须是线性的。不幸的是，当结构受到损伤时，如开裂或屈服，响应过程是非线性的，因此，傅里叶或基于傅里叶的变换方法，如小波分析方法，不能捕捉到振动数据所涉及的物理过程的固有非线性和非平稳特征。美国宇航局古德太空飞行中心最近开发的一种新技术——经验模态分解（EMD）方法和Hlbert谱分析，已被证明为广泛工程应用的非平稳和非线性数据分析提供了巨大的潜力。该项目是加州大学欧文分校和美国宇航局戈达德太空飞行中心之间的合作研究。它旨在进一步发展最近提出的EMD方法和希尔伯特谱分析，以应用于民用基础设施的损伤检测和系统识别。此外，将努力将污染噪声从实测振动数据中分离出来，并捕捉结构响应的固有线性或非线性特征。由结构损伤引起的能量释放所产生的特征也将被识别并用于表征结构损伤的程度。该方法基于创新而强大的数据分析技术，捕捉实测振动数据固有的非线性和非稳态特征，从而进行系统识别和损伤检测。该项目的研究成果有望在民用基础设施的系统识别和损伤检测方面取得重大进展。