The Bayesian Computational Sensor Network methodology is applied to small-scale structural health monitoring. A mobile robot, equipped with vision and ultrasound sensors, maps smallscale structures for damage (e.g., holes, cracks) by localizing itself and the damage in the map. The combination of vision and ultrasound reduces the uncertainty in damage localization. The data storage and analysis takes place exploiting cloud computing mechanisms, and there is also an off-line computational model calibration component which returns information to the robot concerning updated on-board models as well as proposed sampling points. The approach is validated in a set of physical experiments.
We propose the combination of a mobile robot and a computational sensor network approach to perform structural health monitoring of structures. The robot is equipped with piezoelectric sensor actuators capable of sending and receiving ultrasound signals, and explores the surface of a structure to be monitored. A computational model of ultrasound propagation through the material is used to define two structural health monitoring methods: (1) a time reversal damage imaging (TRDI) process, and (2) a damage range sensor (DRS) (i.e., it provides the range to damaged areas in the structure). The damage in the structure is mapped using the DRS approach.
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