Peyman Poozesh

Comparison of Modal Parameters Extracted Using MIMO, SIMO, and Impact Hammer Tests on a Three-Bladed Wind Turbine

As part of a project to predict full-field dynamic strain of rotating structures (e.g. wind turbines or helicopter rotors), a validated numerical model of a structure is required. In this case, a small wind turbine was used. To understand the dynamic characteristics and validate a finite element model of a three-bladed wind turbine, several experimental modal analysis tests were conducted on the turbine attached to a 500-lb steel block. The test structure consisted of three 2.3-m blades mounted to a hub that was attached to the block using a shaft and a lathe chuck. In three separate tests, the structure was excited using a single shaker, multiple shakers, and an impact hammer; the responses of the structure to the excitations were measured using 12 triaxial accelerometers. The results reveal several very closely spaced modes present within the turbine in the test configuration. The natural frequencies and mode shapes obtained by using three different methods were compared to demonstrate the differences (e.g. strengths and weaknesses) between each excitation technique. The paper reports the results obtained and lessons learned during the experimental modal tests of the wind turbine.

A Multi-camera Stereo DIC System for Extracting Operating Mode Shapes of Large Scale Structures

Stereophotogrammetry and three-dimensional (3D) digital image correlation (DIC) have recently received attention for the collection of operating data on large wind turbine blades due to their non-contacting, rapid, and distributed measurement capability. Unlike conventional methods that only provide information at a few discrete points on a wind turbine blade, photogrammetry can provide a wealth of distributed data over the entire structure. One of the challenges with using a camera pair to observe a structure is the limited field of view. Because utility-scale wind turbines are so large and the physical limitations within a blade test facility, a single pair of DIC cameras may not be able to accurately measure the desired area of the structure. Thus, in order to perform a DIC measurement on a utility-scale wind turbine blade, it is desirable to couple several pairs of cameras to simultaneously measure the deformations of the entire blade. The measured deformations of each measured section of the blade needs to be stitched together to extract the deformation for the entire blade. In this paper, a multi-camera 3D DIC measurement is used to identify resonant frequencies and corresponding operating shapes of an individual 2.3-m wind turbine blade placed in a cantilevered boundary condition. The setup is composed of two pairs of synchronized stereo cameras in which each pair of cameras measures a part of the blade’s deformation. The individual measurements include the geometries and the displacements from each pair of cameras that are mapped into a universal coordinate system. Afterwards, operational modal analysis is carried out to extract mode shapes of the cantilevered blade and the extracted results are compared to a validated finite element model for the blade. The results obtained using two camera pairs and DIC demonstrate the great potential of the proposed approach to identify the entire dynamic behavior of utility-scale wind turbine blades.

Structural health monitoring of wind turbine blades using acoustic microphone array

This article proposes a non-contacting measurement technique based on acoustic monitoring to detect cracks or damage within a structure by observing sound radiation using a single microphone or a beamforming array. The technique works by mounting an audio speaker inside a hollow structure, such as a wind turbine blade, and observing the sound radiated from the blade to identify damage. The primary hypothesis for this structural damage detection technique is that the structural damage (cracks, edge splits, holes, etc.) on the surface results in changes in the sound radiation characteristics of the structure. Preliminary measurements to validate the methodology were carried out on a section of a wind turbine blade containing different sized holes and cracks. An acoustic microphone array with 62 microphones was used to measure the sound radiated from the structure when an audio speaker generating random noise was placed inside a cavity emulating a wind turbine blade. A phased array beamforming technique and CLEAN-based subtraction of point spread function from a reference were employed to locate the different damage types on the test structures. The same experiment was repeated using a commercially available 48-channel acoustic ring array to compare the test results. It was shown that both the acoustic beamforming and the CLEAN-based subtraction of point spread function from reference techniques can identify the damage in the test structures with sufficiently high fidelity.

Mode extraction on wind turbine blades via phase-based video motion estimation

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.

A Comparison of Computer-Vision-Based Structural Dynamics Characterizations

As a specific modern non-contact sensing technology, optical/video information is getting more and more attention employed to interpret structural responses and system status awareness. By means of processing the acquired video, a full-field system information is available which may be applied later to Experimental Modal Analysis (EMA), Structural Health Monitoring (SHM), System Identification (SI), etc., while at the same time, there is no influence to the structural testing such as mass loading and stiffness change. There are numerous technologies to extract the dynamic response of structures from acquired videos. In this paper, several point tracking algorithms are particularly compared, including Lucas-Kanade tracker, Hungarian registration algorithm and particle filter. These computer vision algorithms are implemented to extract the natural frequencies of a lab-scale structure, and the efficiency of each method is investigated regarding the consistency in estimating the natural frequencies and computational time. The recorded video contains external noise caused by lighting change during the experiment, as well as the intrinsic uncertainty on the photosensitive devices. Therefore, the natural frequencies estimated via different algorithms will have different values. An overall comparison between several computer vision algorithms are made in this paper in terms of precision, and computational load.

Wind turbine blade health monitoring using acoustic beamforming techniques

Wind turbines operate autonomously and can possess reliability issues attributed to manufacturing defects, fatigue failure, or extreme weather events. In particular, wind turbine blades can suffer from leading and trailing edge splits, holes, or cracks that can lead to blade failure and loss of energy revenue generation. In order to help identify damage, several approaches have been used to detect cracks in wind turbine blades; however, most of these methods require transducers to be mounted on the turbine blades, are not effective, or require visual inspection. This paper will propose a new methodology of the wind turbine non-contact health monitoring using the acoustic beamforming techniques. By mounting an audio speaker inside of the wind turbine blade, it may be possible to detect cracks or damage within the structure by observing the sound radiated from the blade. Within this work, a phased array beamforming technique is used to process acoustic data for the purpose of damage detection. Several algor...

Predicting Full-Field Strain on a Wind Turbine for Arbitrary Excitation Using Displacements of Optical Targets Measured with Photogrammetry

Wind turbine blades and other structures are often subjected to dynamic loading that may not be predicted or measurable at critical locations of interest. Therefore, a non-contacting measurement technique that can provide information throughout an entire structure with the absence of instrumented sensors is desirable. Such an approach is particularly beneficial and relevant to operating rotor or wind turbine blades. In this paper, a three-bladed wind turbine placed in a semi-built-in boundary condition was subjected to a variety of different loadings. The turbine was excited using a sinusoidal excitation, a pluck test, arbitrary impacts on three blades, and random force excitations with a mechanical shaker. The response of the structure to these excitations at optical targets mounted to the blades was measured using three-dimensional point tracking. The limited set of measured displacement at the optical targets was expanded using a modal expansion algorithm. The expanded displacement was used in conjunction with a finite element model of the turbine to extract dynamic strain throughout the entire structure. The results from the technique were compared to instrumented strain gages and are shown be in close agreement. The predicted strain using the proposed approach is not limited to the locations of the optical targets or where the cameras have line of sight. This new technique may enable a new structural health-monitoring approach that has the ability to interrogate an entire structure, inside and outer surface.

Practical Techniques for Scaling of Optically Measured Operating Deflection Shapes

Operational Modal Analysis (OMA) is used to identify vibration patterns of large structures under unknown operating conditions. However, operating data extracted from output-only measurements is not scaled and cannot be used for Structural Dynamic Modification (SDM), frequency response function (FRF) synthesis, force estimation and structural response simulation. Therefore, developing an algorithm that is able to extract scaled mode shapes using measured operating data is desirable. In the current paper, two different scaling techniques including drive point scaling as well as mass sensitivity scaling are employed to scale optically measured operating deflection shapes (ODS). To evaluate the capability of each scaling technique, the scaled optically measured operating shapes are compared to mode shapes extracted using input–output measurements (reference mode shapes). Additionally, the scaled operating shapes are used in structural dynamic modification to demonstrate the benefits and drawbacks associated with the mass sensitivity technique. The results reveal that both mass sensitivity and drive point scaling techniques are capable of effectively scaling optically measured operating deflection shapes of the structure.

An acoustic-array based structural health monitoring technique for wind turbine blades

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.

Vibration-based damage detection in wind turbine blades using Phase-based Motion Estimation and motion magnification

Abstract Vibration-based Structural Health Monitoring (SHM) techniques are among the most common approaches for structural damage identification. The presence of damage in structures may be identified by monitoring the changes in dynamic behavior subject to external loading, and is typically performed by using experimental modal analysis (EMA) or operational modal analysis (OMA). These tools for SHM normally require a limited number of physically attached transducers (e.g. accelerometers) in order to record the response of the structure for further analysis. Signal conditioners, wires, wireless receivers and a data acquisition system (DAQ) are also typical components of traditional sensing systems used in vibration-based SHM. However, instrumentation of lightweight structures with contact sensors such as accelerometers may induce mass-loading effects, and for large-scale structures, the instrumentation is labor intensive and time consuming. Achieving high spatial measurement resolution for a large-scale structure is not always feasible while working with traditional contact sensors, and there is also the potential for a lack of reliability associated with fixed contact sensors in outliving the life-span of the host structure. Among the state-of-the-art non-contact measurements, digital video cameras are able to rapidly collect high-density spatial information from structures remotely. In this paper, the subtle motions from recorded video (i.e. a sequence of images) are extracted by means of Phase-based Motion Estimation (PME) and the extracted information is used to conduct damage identification on a 2.3-m long Skystream® wind turbine blade (WTB). The PME and phased-based motion magnification approach estimates the structural motion from the captured sequence of images for both a baseline and damaged test cases on a wind turbine blade. Operational deflection shapes of the test articles are also quantified and compared for the baseline and damaged states. In addition, having proper lighting while working with high-speed cameras can be an issue, therefore image enhancement and contrast manipulation has also been performed to enhance the raw images. Ultimately, the extracted resonant frequencies and operational deflection shapes are used to detect the presence of damage, demonstrating the feasibility of implementing non-contact video measurements to perform realistic structural damage detection.