ABSTRACT

Structural health monitoring is based on output-only vibration measurements. In order to get an early warning of structural failure, the signal-to-noise ratio of the measurement data should be high. This can be achieved by applying virtual sensing to a redundant sensor network. A Bayesian estimate of the signal of each sensor in the network was derived and proved to be more accurate than the actual measurement. Virtual sensor data replace the actual measurements in damage detection. Another issue is the environmental or operational influences, which can mask the effects of damage. They can be removed by acquiring training data under different conditions and utilizing correlation between the measured quantities. The measurement of the underlying variables is not necessary. Numerical simulations were performed for a structure subject to unknown random excitation and various environmental effects. Damage detection was performed in the time domain using the virtual sensor data.