Composite materials play important roles in multifunctional applications, and thus, the diagnosis of damage patterns in composite materials becomes crucial to avoid critical events" such as structural or functional failures. The impact of an individual damage in composite materials has been extensively studied, however, the interaction of defects/cracks, which leads to critical fracture paths, has not been understood well. In this paper, we develop a Bayesian estimation based statistical analysis technique that estimates the damage pattern of a composite material, in particular, the relative positions of defects in the material, by measuring its through-thickness dielectric properties. We first explain the fundamental dielectric principle that leads to the detection of defect patterns. A capacitance model is then built to measure the material permittivity, and the relationship between the dielectric permittivity and relative positions are found using COMSOL Multiphysics. The interaction effects between defects observed in the simulation are interpreted using the fundamental dielectric principle. A Bayesian estimation based statistical analysis model is then developed to estimate the relative positions of defects in composite materials from the measured global dielectric properties.
Unmanned aerial vehicles (UAVs) play increasing roles in structure health monitoring. With growing mobility in modern Internet-of-Things (IoT) applications, the health monitoring of mobile structures becomes an emerging application. In this paper, we develop a UAV-carried vision-based monitoring system that allows a UAV to continuously track and monitor a mobile infrastructure and transmit back the monitoring information in real- time from a remote location. The monitoring system uses a simple UAV-mounted camera and requires only a single feature located on the mobile infrastructure for target detection and tracking. The computation-effective vision-based tracking solution based on a single feature is an improvement over existing vision-based lead-follower tracking systems that either have poor tracking performance due to the use of a single feature, or have improved tracking performance at a cost of the usage of multiple features. In addition, a UAV-carried aerial networking infrastructure using directional antennas is used to enable robust real-time transmission of monitoring video streams over a long distance. Automatic heading control is used to self-align headings of directional antennas to enable robust communication in mobility. Compared to existing omni-communication systems, the directional communication solution significantly increases the operation range of remote monitoring systems. In this paper, we develop the integrated modeling framework of camera and mobile platforms, design the tracking algorithm, develop a testbed of UAVs and mobile platforms, and evaluate system performance through both simulation studies and field tests.