Six wind turbines were blown to the ground by the wind gust during the attack of Typhoon Soudelor in August 2015. Survey using unmanned aerial vehicle, UAV, found the collapsed wind turbines had been broken at the lower section of the supporting towers. The dynamic behavior of wind turbine systems is thus in need of attention. The vibration of rotor blades and supporting towers of two wind turbine systems have been measured remotely using IBIS, a microwave interferometer. However the frequency of the rotor blade can be analyzed only if the microwave measurements are taken as the wind turbine is parked and secured. Time-frequency analyses such as continuous wavelet transform and reassigned spectrograms are applied to the displacement signals obtained. A frequency of 0.44Hz exists in both turbines B and C at various operating conditions. Possible links between dynamic characteristics and structural integrity of wind turbine -tower systems is discussed.
Longitudinal rail force management of continuous welded rail (CWR) is important for safe and efficient railroad operation. A key parameter to measure and monitor is the rail neutral temperature (RNT) or the stress-free temperature. The team proposed a supervised learning framework to estimate the RNT using impulse vibrational responses from CWRs. We first established an instrumented field site on a revenue-service line and collected impulse vibrational response data covering a wide range of temperature and thermal stress. Then, we trained a data-driven model that uses rail temperatures and modal frequencies as the input for in-situ RNT prediction. The results demonstrated that the proposed framework could provide RNT estimation with a reasonable precision (±5 ºF)
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