Model validity and frequency band selection in operational modal analysis
Model validity and frequency band selection in operational modal analysis
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DOI：
10.1016/j.ymssp.2016.03.025
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发表时间：
2016-12
影响因子：
8.4
通讯作者：
S. Au
中科院分区：
文献类型：
--
作者：
S. Au
实验模态分析的目的是利用振动测量来识别结构的固有频率、阻尼比、振型等。在频域中工作时会遇到两个基本问题：在特定频率附近是否存在模式？如果是这样的话，在不引起显著建模误差的情况下，可以包括多少频率附近的频谱数据来进行模式识别？对于高信噪比(S/n)的数据，可以使用奇异值频谱等经验工具来解决这些问题。否则，它们通常是开放的并且可能是具有挑战性的，例如对于具有低S/n比的模式或关闭模式。在这项工作中，这些问题是使用贝叶斯方法解决的。重点放在业务模式分析上，即只有输出的环境数据，其中识别不确定性和建模误差可能很大，它们的控制要求最高。该方法导致“证据比率”量化相互竞争的几组建模假设的相对可信程度。后者涉及对“如果不是怎么办”的情况进行建模，这种情况不是微不足道的，但可以通过系统地考虑替代模型和使用最大熵原理来解决。综合数据和现场数据被认为是为了调查证据比率的行为，以及在实际应用中应该如何解释它们。
Experimental modal analysis aims at identifying the modal properties (e.g., natural frequencies, damping ratios, mode shapes) of a structure using vibration measurements. Two basic questions are encountered when operating in the frequency domain: Is there a mode near a particular frequency? If so, how much spectral data near the frequency can be included for modal identification without incurring significant modeling error? For data with high signal-to-noise (s/n) ratios these questions can be addressed using empirical tools such as singular value spectrum. Otherwise they are generally open and can be challenging, e.g., for modes with low s/n ratios or close modes. In this work these questions are addressed using a Bayesian approach. The focus is on operational modal analysis, i.e., with ‘output-only’ ambient data, where identification uncertainty and modeling error can be significant and their control is most demanding. The approach leads to ‘evidence ratios’ quantifying the relative plausibility of competing sets of modeling assumptions. The latter involves modeling the ‘what-if-not’ situation, which is non-trivial but is resolved by systematic consideration of alternative models and using maximum entropy principle. Synthetic and field data are considered to investigate the behavior of evidence ratios and how they should be interpreted in practical applications.