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Sparse sensor networks for Lamb wave-based structural health monitoring (SHM) can detect defects in plate-like structures. However, the limited number of sensor positions provides little information to characterize the unknown scatterer. This can be achieved by full wavefield analysis e.g. using Laser Doppler vibrometry measurements.
This paper proposes deconvolution processing that enhances the acoustic wavefield interpretation by increasing the temporal resolution of the underlying ultrasound signals. Applying this preprocessor to the whole wavefield allows improved non-destructive assessment of the defect. This approach is verified experimentally through a case study on an isotropic aluminum plate with four cracks.
The article presents the results of numerical and experimental investigations of guided wave propagation in aluminum plates with variable thickness. The shapes of plate surfaces have been specially designed and manufactured using a CNC milling machine. The shapes of the plates were defined by sinusoidal functions varying in phase shift, which forced the changes in thickness variability alongside the propagation path. The main aim of the study is to analyze the wave propagation characteristics caused by non-uniform thickness. In the first step, the influence of thickness variability on the time course of propagating waves has been analyzed theoretically. The study proves that the wave propagation signals can be determined based on knowledge about the statistical description of the specimen geometry. The histograms of thickness distribution together with the a priori knowledge of the dispersion curves were used to develop an iterative procedure assuming that the signal from the previous step becomes the excitation in the next step. Such an approach allowed for taking into account the complex geometry of the plate and rejecting the assumption about the constant average thickness alongside the propagation path. In consequence, it was possible to predict correctly the signal time course, as well as the time of flight and number of propagating wave modes in specimens with variable thickness. It is demonstrated that theoretical signals predicted in this way coincide well with numerical and experimental results. Moreover, the novel procedure allowed for the correct prediction of the occurrence of higher-order modes.
This work aims at radar sensors in the frequency band from 57 to 64 GHz that can be embedded in wind turbine blades during manufacturing, enabling non-destructive quality inspection directly after production and structural health monitoring (SHM) during the complete service life of the blade. In this paper, we show the fundamental damage detection capability of this sensor technology during fatigue testing of typical rotor blade materials. Therefore, a frequency modulated continuous wave (FMCW) radar sensor is used for damage diagnostics, and the results are validated by simultaneous camera recordings. Here, we focus on the failure modes delamination, fiber waviness (ondulation), and inter-fiber failure. For each failure mode, three samples have been designed and experimentally investigated during fatigue testing. A damage index has been proposed based on residual, that is, differential, signals exploiting measurements from pristine structural conditions. This study shows that the proposed innovative radar approach is able to detect continuous structural degradation for all failure modes by means of gradual signal changes.
This study presents an ultra-wideband, elliptical slot, planar monopole antenna for early breast cancer microwave imaging. The on-body antenna's operation is optimised by direct contact with the patient's skin. With a compact size of 9 × 7 mm, the antenna covers a wide bandwidth from 16 to 24 GHz for reflection coefficients lower than –10 dB. Besides, it also features an electrode for electrical impedance tomography applications. Verification on a volunteer's breast gives an excellent agreement with the simulation for the defined bandwidth. Furthermore, as the first stage of the system's characterisation, pork fat is also used to demonstrate the possibility to enhance the transmission between the antennas within the high loss environment. Those results propose the feasibility of implementing a high-frequency radar system for breast cancer detection.
Radar technology in the millimeter-wave frequency band offers many interesting features for wind park surveillance, such as structural monitoring of rotor blades or the detection of bats and birds in the vicinity of wind turbines (WTs). Currently, the majority of WTs are affected by shutdown algorithms to minimize animal fatalities via direct collision with the rotor blades or barotrauma effects. The presence of rain is an important parameter in the definition of those algorithms together with wind speed, temperature, time of the day, and season of the year. A Ka-band frequency-modulated continuous-wave radar (33.4-36.0 GHz) installed at the tower of a 2-MW WT was used during a field study. We have observed characteristic rain-induced patterns, based on the range-Doppler algorithm. To better understand those signatures, we have developed a laboratory experiment and implemented a numerical modeling framework. Experimental and numerical results for rain detection and classification are presented and discussed here. Based on this article, a bat- and bird-friendly adaptive WT control can be developed for improved WT efficiency in periods of rain and, at the same time, reduced animal mortality.