Online automatic monitoring of abnormal vibration of stay cables based on acceleration data from structural health monitoring

•An automatic identification method of abnormal vibrations for stay-cables is proposed.•Two feature indices distinguishing the VIV from ambient vibration are proposed.•Minimum Euclidean Distance Classifier is utilized to automate the identification process.•Field monitoring data are used to validate the feasibility. Online automatic monitoring of abnormal vibrations, such as vortex-induced vibration (VIV) and high amplitude vibration (HAV), of stay-cables is important for bridge maintenance. However, the existing methods either require manual intervention or yield unreasonable identification results and are not suitable for online automatic monitoring. This study proposes a fully automatic method to identify the abnormal vibrations of stay-cables based on acceleration data. Two quantified feature indices are extracted from frequency domain and complex domain to characterize the VIV. A feature vector is established to distinguish the VIV from ambient vibration, and Minimum Euclidean Distance Classifier is utilized to automate the online identification process. The HAV is automatically identified based on root mean square of acceleration. The proposed method avoids any manual intervention and can yield correct and convincible identification results. Case study of a cable-stayed bridge finally validates the feasibility and novelty of the proposed method.