Bayesian Spectral Decomposition Method for Operational Modal Identification in Wireless Sensor Network

Abstract eng:
Structural health monitoring (SHM) employing wireless sensor networks (WSN) is becoming increasingly popular in recent years. A Bayesian spectral decomposition (BSD) method employing a distributed computing strategy is presented for structural modal identification in WSN using output-only response data. This method uses the statistical properties of the largest eigenvalue of the output spectral matrix to obtain not only the optimal values of the updated modal frequencies and damping ratios but also their associated uncertainties by calculating the posterior joint probability distribution of these parameters. Mode shapes are obtained by singular value decomposition (SVD) of the output spectral matrix at corresponding discrete frequencies closest to their optimal values. This method identifies each mode, the modal frequency and damping ratio and the mode shape separately, which takes advantage of variable separation and can distribute the computational effort to several computational units, thus becoming suitable for implementation in wireless sensor network that provides such distributed computing environment. In addition, energy is conserved through the use of a novel distributed computing strategy. The efficacy and efficiency of the proposed methodology is demonstrated using numerical simulations.

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