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Model validity and frequency band selection in operational modal analysis

机译：运行模态分析中的模型有效性和频带选择

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摘要

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) 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 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.
机译：实验模态分析旨在使用振动测量来识别结构的模态特性（例如固有频率，阻尼比，模态形状）。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，在不引起明显建模误差的情况下，可以在频率附近包含多少频谱数据用于模式识别？对于具有高信噪比（s / n）的数据，可以使用经验工具（例如奇异值频谱）解决这些问题。否则，它们通常是开放的，并且可能具有挑战性，例如，对于低信噪比的模式或封闭模式。在这项工作中，使用贝叶斯方法解决了这些问题。重点是操作模态分析，即使用“仅输出”环境数据，其中识别不确定性和建模误差可能很大，并且对它们的控制要求最高。该方法导致“证据比率”量化了竞争性建模假设集的相对合理性。后者涉及对“如果……不是”情况进行建模，这是不平凡的，但可以通过系统地考虑替代模型并使用最大熵原理来解决。考虑使用合成数据和现场数据来调查证据比率的行为以及在实际应用中应如何解释它们。

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