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Development of self-monitoring structural composites with integrated sensing networks


There is a growing need for the development of in-service structural health monitoring systems to rapidly assess the health condition and durability of composite structures. Recent progress in sensor technologies, signal processing and electronics has made it possible to fulfill this demand. The present work seeks to develop an intelligent composite, by integrating a micro-sensor array and network communication nodes that enables self-sensing and self- damage-diagnosis capabilities without compromising the structural integrity of the composite. The integrity and mechanical behavior of unidirectional fiber glass/epoxy composites with integrated simulated sensors are investigated. Particularly, the damage initiation within the material is sought numerical study and verified by experimental observations. The issue of data management through the development of localized processing algorithms is also addressed. A feature extraction algorithm using the spatially distributed 2D CWT is proposed for a sensor network to automatically detect and locate the local features around the damage sites. The implementation of this algorithm into the Nearest-neighbor mesh network is also demonstrated. Experimental and numerical studies are carried out to characterize the acoustic wave propagation in thin glass fiber/epoxy composite plates for the application of Acoustic Emission (AE) technique for health monitoring. Finally, a prototype sensor network system using an array of polyvinilidene (PVDF) sensors and a 4- tree micro-controller network architecture is presented. A damage identification approach based on the AE technique and pre-calibrated look-up tables is thus proposed to be implemented to our sensor network system. The advantage of such a network system is the low bandwidth and computational power demands since the detection and identification of an anomaly is performed locally, in- network, rather than using an external processor

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