Several noninvasive imaging techniques have been developed to monitor the health of skin and enhance the diagnosis of skin diseases. Among them, skin elastography is a popular technique used to measure the elasticity of the skin. A change in the elasticity of the skin can influence its natural frequencies and mode shapes. We propose a technique to use the resonant frequencies and mode shapes of the skin to monitor its health. Our study demonstrates how the resonant frequencies and mode shapes of skin can be obtained using numerical and experimental analysis. In our study, natural frequencies and mode shapes are obtained via two methods: (1) finite element analysis: an eigensolution is performed on a finite element model of normal skin, including stratum corneum, epidermis, dermis, and subcutaneous layers and (2) digital image correlation (DIC): several in-vivo measurements have been performed using DIC. The experimental results show a correlation between the DIC and FE results suggesting a noninvasive method to obtain vibration properties of the skin. This method can be further examined to be eventually used as a method to differentiate healthy skin from diseased skin. Prevention, early diagnosis, and treatment are critical in helping to reduce the incidence, morbidity, and mortality associated with skin cancer; thus, making the current study significant and important in the field of skin biomechanics.
Phase-based motion estimation and video magnification are non-contact and target-less efficient approaches that are being used to extract the operating deflection shapes of vibrating structures. In recent years, Operational Modal Analysis (OMA) and Experimental Modal Analysis (EMA) as the most well studied structural dynamics identification tools have benefited from the unique advantages that phase-based motion estimation and video magnification can offer. Within this study, the phase based motion estimation and video magnification techniques are adopted to perceive the operational deflection shapes and dominant vibration patterns in a Wind Turbine Blade (WTB) cross-section. Moreover, the estimated resonant frequencies are validate by the commercial accelerometer measurements as well that indicates the reliability of the results provided by phase-based motion estimation and video magnification. The identified vibrational operating deflection shapes and the resonant frequencies can be valuable information for design enhancement, as well as finite element model updating and model validation. Moreover, subcomponent structural studies can utilize the results obtained from the vision-based OMA test on the cross-section of the wind turbine blade.
In recent years, image processing techniques are being applied more often for structural dynamics identification, characterization, and structural health monitoring. Although as a non-contact and full-field measurement method, image processing still has a long way to go to outperform other conventional sensing instruments (i.e. accelerometers, strain gauges, laser vibrometers, etc.,). However, the technologies associated with image processing are developing rapidly and gaining more attention in a variety of engineering applications including structural dynamics identification and modal analysis. Among numerous motion estimation and image-processing methods, phase-based video motion estimation is considered as one of the most efficient methods regarding computation consumption and noise robustness. In this paper, phase-based video motion estimation is adopted for structural dynamics characterization on a 2.3-meter long Skystream wind turbine blade, and the modal parameters (natural frequencies, operating deflection shapes) are extracted. Phase-based video processing adopted in this paper provides reliable full-field 2-D motion information, which is beneficial for manufacturing certification and model updating at the design stage. The phase-based video motion estimation approach is demonstrated through processing data on a full-scale commercial structure (i.e. a wind turbine blade) with complex geometry and properties, and the results obtained have a good correlation with the modal parameters extracted from accelerometer measurements, especially for the first four bending modes, which have significant importance in blade characterization.
This paper proposes a non-contact measurement technique for health monitoring of wind turbine blades using acoustic beamforming techniques. The technique works by mounting an audio speaker inside a wind turbine blade and observing the sound radiated from the blade to identify damage within the structure. The main hypothesis for the structural damage detection is that the structural damage (cracks, edge splits, holes etc.) on the surface of a composite wind turbine blade results in changes in the sound radiation characteristics of the structure. Preliminary measurements were carried out on two separate test specimens, namely a composite box and a section of a wind turbine blade to validate the methodology. The rectangular shaped composite box and the turbine blade contained holes with different dimensions and line cracks. An acoustic microphone array with 62 microphones was used to measure the sound radiation from both structures when the speaker was located inside the box and also inside the blade segment. A phased array beamforming technique and CLEAN-based subtraction of point spread function from a reference (CLSPR) were employed to locate the different damage types on both the composite box and the wind turbine blade. The same experiment was repeated by using a commercially available 48-channel acoustic ring array to compare the test results. It was shown that both the acoustic beamforming and the CLSPR techniques can be used to identify the damage in the test structures with sufficiently high fidelity.
Health monitoring of wind turbines is typically performed using conventional sensors (e.g. strain-gages and accelerometers) that are usually mounted to the nacelle or gearbox. Although many wind turbines stop operating due to blade failures, there are typically few to no sensor mounted on the blades. Placing sensors on the rotating parts of the structure is a challenge due to the wiring and data transmission constraints. Within the current work, an approach to monitor full-field dynamic response of rotating structures (e.g. wind turbine blades or helicopter rotors) is developed and experimentally verified. A wind turbine rotor was used as the test structure and was mounted to a block and horizontally placed on the ground. A pair of bearings connected to the rotor shaft allowed the turbine to freely spin along the shaft. Several optical targets were mounted to the blades and a pair of high-speed cameras was used to monitor the dynamics of the spinning turbine. Displacements of the targets during rotation were measured using three-dimensional point tracking. The point tracking technique measured both rigid body displacement and flexible deformation of the blades at target locations. While the structure is rotating, only flap displacements of optical targets (displacements out of the rotation plane) were used in strain prediction process. The measured displacements were expanded and applied to the finite element model of the turbine to extract full-field dynamic strain on the structure. The proposed approach enabled the prediction of dynamic response on the outer surface as well as within the inner points of the structure where no other sensor could be easily mounted. In order to validate the proposed approach, the predicted strain was compared to strain measured at four locations on the spinning blades using a wireless strain-gage system.