Abstract
Structural health monitoring is based on output-only vibration measurements acquired by a sensor network. In order to get an early warning of structural failure, high accuracy of measurement data is required. Empirical virtual sensing techniques can be applied to reduce the measurement error. The signal of each sensor can estimated using the current sensor network data. Due to hardware redundancy, the estimate is more accurate than the actual measurement. Two different estimates are studied: (1) minimum mean square error (MMSE) estimate, or the prior mean, and (2) Bayesian estimate, or the posterior mean. The measurement data are replaced with the estimated data in a damage detection algorithm. Numerical simulations were performed for a structure subject to unknown random excitation. Damage detection was based on the data from virtual sensors. Both algorithms outperformed the reference method with no virtual sensing. Algorithm 2 gave better results than algorithm 1 but it requires that the measurement error is known, whereas algorithm 1 needs no additional information.
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