Decentralized modal identification of structures using an adaptive empirical mode decomposition method

With recent advancement of robotic technology, mobile wireless devices have made a paradigm shift in cost-effective and faster deployment of sensors towards health monitoring of large-scale infrastructure. A wide range of system identification methods has been developed by the researchers to accurately identify unknown structural parameters from the measured vibration data. However, most of these techniques are suitable only when all key locations of the structure are instrumented. In case of decentralized mobile sensing network where a sensor is autonomously moved from one location to another, only a single sensor is available at a particular time. In this paper, a newer time-frequency analysis method, namely Empirical Mode Decomposition (EMD), is explored and improved to undertake system identification using single channel measurement. Traditional EMD results in significant mode-mixing while analyzing closely-spaced modes and data with measurement noise. In this paper, Time-Varying Filtering based Empirical Mode Decomposition (TVF-EMD) is proposed to perform modal identification using decentralized sensing approach. The proposed method is fully adaptive and suitable for automation since it uses only one channel of data at a time. The proposed method is verified using a suite of numerical, experimental and full-scale studies using wireless sensors in a decentralized manner. (C) 2019 Elsevier Ltd. All rights reserved.