This paper develops a framework of a cognitive sensor networks system for structure defect monitoring and classification
using guided wave signals. Guided ultrasonic waves that can propagate long distances along civil structures have been
widely studied for inspection and detection of structure damage. Smart ultrasonic sensors arranged as a spatially distributed
cognitive sensor networks system can transmit and receive ultrasonic guided waves to interrogate structure defects such
as cracks and corrosion. A distinguishing characteristic of the cognitive sensor networks system is that it adaptively
probes and learns about the environment, which enables constant optimization in response to its changing understanding
of the defect response. In this paper, we develop a sequential multiple hypothesis testing scheme combined with adaptive
waveform transmission for defect monitoring and classification. The performance is verified using numerical simulations
of guided elastic wave propagation on a pipe model and by Monte Carlo simulations for computing the probability of
correct classification.
KEYWORDS: Sensors, Data acquisition, Optical proximity correction, Human-machine interfaces, Control systems, Data modeling, OLE for process control, Systems modeling, Data processing, Data storage
Using control area network (CAN) technique and open connectivity (OPC) method, a sensor fieldbus is developed to acquire and preprocess the data came from structure for monitoring the damage. The OPC interface is added in sensor bus for information sharing. The algorithm of distance of storing-strategy data is embedded in the sensor fieldbus. A system of data acquisition and preprocessing based on the sensor fieldbus is presented and simulated it on the offshore platform. The result shows that the speed and efficiency of sensor fieldbus are reliable and robust when the gigantic data stream into the monitoring system.
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