Abstract

Automated operational modal analysis (OMA) is attractive and has been extensively used to replace traditional OMA, which requires much empirical observation and engineers’ judgment. Still, the uncertainties on modal parameters and spurious modes are challenging to estimate under the field conditions. For addressing this challenge, this research proposed an automated modal identification approach. The proposed approach consists of two steps: (1) modal analysis using covariance-driven stochastic subspace algorithm; (2) automated interpretation of the stabilization diagram. An additional uncertainty criterion is employed to initially remove as many spurious modes as possible. A novel threshold calculation for clustering is proposed with incorporating uncertainty of modal parameters and the weighting factor. An improved self-adaptive clustering with new distance calculation is used to group physical modes, followed by the final step of robust outlier detection to select outlying modes. The proposed automated approach requires minimum human intervention. Two field tests of the footbridge and a post-tensioned concrete bridge are used to verify the proposed approach. A modal tracking was used for continuously measured data for demonstrating the applicability of the approach. Results show the proposed approach has fairly good performance and be suitable for automated OMA and long-term health monitoring.

摘要

自动化操作模态分析（OMA）具有吸引力，已被广泛用于替代需要大量经验观察和工程师判断的传统OMA。尽管如此，在现场条件下估计模态参数和杂散模式的不确定性仍然具有挑战性。为了应对这一挑战，本研究提出了一种自动模态识别方法。所提出的方法包括两个步骤：（1）使用协方差驱动的随机子空间算法进行模态分析；(2) 稳定图的自动解释。一个附加的不确定性标准被用来在最初去除尽可能多的杂散模式。提出了一种结合模态参数不确定性和加权因子的聚类阈值计算方法。使用新的距离计算改进的自适应聚类对物理模式进行分组，然后是鲁棒异常检测的最后一步以选择离群模式。建议的自动化方法需要最少的人工干预。人行桥和后张混凝土桥的两个现场测试用于验证所提出的方法。模态跟踪用于连续测量数据，以证明该方法的适用性。结果表明，该方法具有相当好的性能，适用于自动化 OMA 和长期健康监测。人行桥和后张混凝土桥的两个现场测试用于验证所提出的方法。模态跟踪用于连续测量数据，以证明该方法的适用性。结果表明，该方法具有相当好的性能，适用于自动化 OMA 和长期健康监测。人行桥和后张混凝土桥的两个现场测试用于验证所提出的方法。模态跟踪用于连续测量数据，以证明该方法的适用性。结果表明，该方法具有相当好的性能，适用于自动化 OMA 和长期健康监测。