Bridge Modal Parameter Identification from UAV Measurement Based on Empirical Mode Decomposition and Fourier Transform

Abstract

This paper proposes two approaches, Empirical Mode Decomposition (EMD) and Fourier Transform (FT), to correct the vibration signals measured by an Unmanned Aerial Vehicle (UAV), which overcomes the difficulty of selection of reference points used in other correction methods, such as homography transformation and three-dimensional reconstruction. In the method of this paper, a UAV is used to collect the video of a vibrated bridge, and the displacement signal of the bridge is obtained from the video by Kanade–Lucas–Tomasi (KLT) optical flow method, which contains false displacement caused by the ego-motion of the UAV during the measurement. The false displacement can be effectively eliminated by EMD and FT to obtain the real displacement signal. Finally, the displacement signal is processed by the Operational Modal Analysis (OMA) technique to obtain the bridge modal parameters. The performance of correcting vibration signals and extracting bridge modal parameters from the vibration signals based on EMD, FT, and Differential Filtering (DF) are compared by taking the fixed camera measurement as a reference (the accuracy of measuring bridge vibration with fixed cameras has been verified) in this paper, and it is demonstrated that EMD has better reliability in processing signal measured by UAVs, which is mainly due to the absence of random factors and too much noise in the signal processing process of EMD.