基于贝叶斯推理的长期连续模态自动跟踪算法

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Siyuan Sun, Bin Yang, Qilin Zhang, R. Wüchner, Licheng Pan, Haitao Zhu
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引用次数: 0

摘要

模态跟踪在结构健康监测中起着至关重要的作用,因为模态参数的变化有助于我们了解结构的动力特性,并可能反映结构性能的潜在恶化。尽管存在许多模态参数估计(MPE)方法,但不能保证MPE过程在长期监测期间每次都能排除所有杂散模态并且不丢失任何物理模态。结构相对较大的阻尼、较差的数据质量以及结构模态参数的显著变化可能会使估计的模态参数出现虚假、缺失或错误分类。它使长期模态跟踪半自动化或手动,这限制了及时的下游应用,如异常检测、状态评估和决策制定。本研究旨在提出一种基于贝叶斯推理的模态长期连续自动跟踪算法,即使模态参数、阻尼和数据质量发生显著变化。利用贝叶斯推理从现有的MPE方法的结果中确定物理模态。贝叶斯模型考虑了从最近的响应集识别的模态和从多个以前的响应集识别的模态概率模型,以便更好地从MPE结果确定物理模态。此外,与一般模态跟踪算法相比,该算法只需要三个额外的超参数,并且可以通过网格搜索方法快速确定它们。通过数值算例和Z24桥梁实例验证了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Long-term continuous automatic modal tracking algorithm based on Bayesian inference
Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure’s dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark.
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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