Analytical Mode Decomposition of Dynamic Structural Response for The Detection of Damage

用于损伤检测的动态结构响应的分析模式分解

• 批准号: 1538416
• 负责人: Genda Chen
• 金额: $ 26.93万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1538416&HistoricalAwards=false
• 关键词: Analytical; Mode; Decomposition; Dynamic; Structural

Damage detection is the process of identifying the occurrence of changes to the material and/or geometric properties of a structural system that adversely affect performance. The goal is to transform subjective and inconsistent visual inspections into objective and reliable condition screening of degrading structures, in particular during extreme events such as earthquakes and blasts. Most techniques currently available depend on the establishment of a structural model with no damage progression over time, which is often unrealistic during extreme events. This award supports fundamental research for the development of a model-free and adaptive data analysis technique. The new technique will enable an accurate and robust evaluation of time-dependent structural conditions, which has been increasingly sought for applications in civil infrastructure immediately after a disastrous event. Therefore, results from this study will greatly benefit post-event response and recovery, leading to enhanced resiliency and stable economy. The technique will be made available through a Wikipedia page and may be applicable to multiple disciplines in science, engineering, healthcare and finance industries. These multi-disciplinary applications will help broaden the participation of underrepresented groups in research and positively impact engineering education.Empirical mode decomposition is a non-parametric and adaptive damage detection technique that has since 1996 been demonstrated powerful to deal with nonstationary and nonlinear dynamic responses. However, it has never been mathematically proven for guaranteed success in various applications and thus faced challenges in analyzing noisy data, weak intermittent fluctuations, closely-spaced modes of vibration, and temporal changes in vibration mode. This research is to provide a mathematical foundation to the decomposition of structural dynamic response and explore analytical mode decomposition for damage detection of time-varying structures. The research team will establish a sampling rate and finite data length criterion that guarantees an accurate decomposition of a discrete time sequence, develop a new adaptive wavelet transform for the optimal determination of time-dependent bisecting frequencies, derive a computationally efficient solution to a new system-response characteristics relationship recently discovered for nonlinear structures, and experimentally validate the robustness, accuracy, and efficiency of the analytical decomposition in system identification and damage detection of complex structures with different nonlinearities.

损伤检测是确定结构系统的材料和/或几何属性是否发生了对性能产生不利影响的更改的过程。其目标是将主观和不一致的视觉检查转变为对退化结构的客观和可靠的条件筛选，特别是在地震和爆炸等极端事件期间。目前可用的大多数技术依赖于建立一个没有随着时间推移的损伤发展的结构模型，这在极端事件中往往是不现实的。该奖项支持开发无模型和自适应数据分析技术的基础研究。这项新技术将使对随时间变化的结构状况的准确和可靠的评估成为可能，在灾难性事件发生后，越来越多的人寻求将其应用于民用基础设施。因此，这项研究的结果将极大地有利于灾后响应和恢复，从而增强弹性和稳定经济。这项技术将通过维基百科页面提供，可能适用于科学、工程、医疗保健和金融行业的多个学科。这些多学科的应用将有助于扩大未被充分代表的群体在研究中的参与，并对工程教育产生积极的影响。经验模式分解是一种非参数和自适应的损伤检测技术，自1996年以来已被证明是处理非平稳和非线性动态响应的有效方法。然而，它从未在数学上被证明在各种应用中保证了成功，因此在分析噪声数据、微弱的间歇性波动、紧密间隔的振动模式和振动模式的时间变化方面面临挑战。本研究旨在为结构动力响应分解提供数学基础，探索时变结构损伤识别的解析模式分解方法。研究小组将建立确保离散时间序列准确分解的采样率和有限数据长度准则，开发一种新的自适应小波变换来优化确定随时间变化的平分频率，推导出最近发现的针对非线性结构的新的系统-响应特征关系的计算高效解，并通过实验验证解析分解在具有不同非线性的复杂结构的系统识别和损伤检测中的鲁棒性、准确性和效率。