Understanding and managing identification uncertainty of close modes in operational modal analysis
Understanding and managing identification uncertainty of close modes in operational modal analysis
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DOI：
10.1016/j.ymssp.2020.107018
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发表时间：
2021-01-15
影响因子：
8.4
通讯作者：
Raby, Alison
中科院分区：
文献类型：
--
作者：
Au, Siu-Kui;Brownjohn, James M. W.;Raby, Alison
接近模式比分离良好的模式更难以识别，并且它们的识别（ID）结果通常具有明显更大的不确定性或可变性。在操作模态分析 (OMA) 中，情况变得更具挑战性，这是目前获取大型土木结构现场动态特性最经济可行的方法，也是最需要 ID 不确定性管理的地方。为了理解 ID 不确定性并在现场测试计划中管理它，这项工作开发了闭模态的“不确定性定律”，即使用仅输出环境振动数据识别的模态参数的剩余不确定性的闭式解析表达式。这些表达式揭示了一个基本定义，该定义量化了“接近程度”，并揭开了各种控制因素的作用。结果通过合成、实验室和现场数据进行验证。现场数据控制因素的统计揭示了 OMA 在不同情况下面临的挑战，现在在一个连贯的概率框架内负责。建议在规划环境振动测试时考虑接近模式。根据建模假设和概率的使用，不确定性定律决定了模态属性可实现的精度，而与使用的 ID 算法无关。本文结果背后的数学理论在另一篇论文中介绍。 (C) 2020 作者。由爱思唯尔有限公司出版
Close modes are much more difficult to identify than well-separated modes and their identification (ID) results often have significantly larger uncertainty or variability. The situation becomes even more challenging in operational modal analysis (OMA), which is currently the most economically viable means for obtaining in-situ dynamic properties of large civil structures and where ID uncertainty management is most needed. To understand ID uncertainty and manage it in field test planning, this work develops the 'uncertainty law' for close modes, i.e., closed form analytical expressions for the remaining uncertainty of modal parameters identified using output-only ambient vibration data. The expressions reveal a fundamental definition that quantifies 'how close is close' and demystify the roles of various governing factors. The results are verified with synthetic, laboratory and field data. Statistics of governing factors from field data reveal OMA challenges in different situations, now accountable within a coherent probabilistic framework. Recommendations are made for planning ambient vibration tests taking close modes into account. Up to modelling assumptions and the use of probability, the uncertainty law dictates the achievable precision of modal properties regardless of the ID algorithm used. The mathematical theory behind the results in this paper is presented in a companion paper. (C) 2020 The Authors. Published by Elsevier Ltd.