A two-stage automated OMA framework for transmission towers based on clustering algorithms

• Published in: Structures (Oxford) Vol. 61; p. 106023
• Main Authors: Feng, Yuhu, Su, Youhua, Zhao, Chao, Zhu, Yanming, Sun, Qing
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2024
• Subjects: Clustering algorithms; Operational modal analysis; Stochastic subspace identification; Structural health monitoring; Transmission towers; Structural health monitoring; Transmission towers; Clustering algorithms; Stochastic subspace identification; Operational modal analysis
• ISSN: 2352-0124; 2352-0124
• DOI: 10.1016/j.istruc.2024.106023

Automated operational modal analysis (OMA) is a significant basis for structural health monitoring (SHM) of transmission towers. There have been many effective uses of automated OMA algorithms during the past decade. However, several unresolved issues still require further investigation, particularly in the following areas: i) Minimizing the number of parameters to achieve full automation; ii) Defining the optimal parameters to significantly reduce spurious modes; iii) Exploring an automated OMA framework for transmission towers. In this study, we propose an automated OMA method for transmission towers based on the clustering algorithms with the stochastic subspace identification (SSI) approach. The proposed method could be summarized in two stages: the “rough” and “enhanced” stages. In the “rough” stage, the modes identified by SSI are divided into possible physical modes and certain spurious modes by the k-means algorithm considering the physical meaning of the modal parameters. In the “enhanced” stage, similar physical modes are classified by data-driven hierarchical clustering, and the outliers of each cluster are eliminated. Then, the applicability and efficiency of the proposed method are verified by the numerical simulation. Finally, the method is applied to a ± 500 kV transmission tower to identify the modal parameters with the field ambient vibration test data. Identification results are compared with manual identification to demonstrate the improvement and contribution of the automated OMA. The results show that the proposed framework has the capacity to automatically eliminate spurious modes and discrimination in the case of the close modes. The proposed method could accurately and automatically identify modal parameters to avoid the dependence on the experience and the uncertainty of manual identification.