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 noncontact 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-meter 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.

Computer Vision Aided Structural Identification: Feature Tracking Using Particle Tracking Velocimetry versus Optical Flow

“…Optical flow [6,7] is the pattern of apparent motion between two consecutive images caused by movement of an object or the camera. It assumes consistent brightness between two consecutive frames.…”

“…If the selected frequency band lies within a natural frequency of the structure, the resultant magnified motion reveals an approximation of its respective mode shape. Within the SHM domain, this technique was initially used as a qualitative tool for modeshape visualization [3] but it was later together with its variants adopted for quantitative structural identification problems [4][5][6][7][8].…”

Estimation of full‐field dynamic strains from digital video measurements of output‐only beam structures by video motion processing and modal superposition

“…Using image processing algorithms such as optical flow 21 and digital image correlation (DIC), 22 digital video camera imaging measurements have been successfully used for discrete structural dynamics response measurements 19,[23][24][25][26][27][28][29][30] or full-field (as many measurement points as the pixel number of the video frame on the structure) structural dynamics response measurements and subsequently experimental modal analysis. 20,[31][32][33][34][35][36][37] However, many of these methods 38 typically rely on installing and tracking the optical markers or speckle paints on the structure's surface, which becomes less practical for large measurement area or in harsh environment (high temperature and corrosion). In addition, they are computationally extremely extensive.…”

Operational Deflection Shape Extraction from Broadband Events of an Aircraft Component Using 3D-DIC in Magnified Images

“…is methodology has been employed by Chen et al to describe operational deflection shapes (ODS) under forced normal mode tests using the edge detection algorithm to highlight the magnified shapes [12]. Sarrafi et al [13] also employed the edge detection on magnified images for damage detection by noticing changes in natural frequencies and ODSs. PMM has also been employed to develop an autonomous methodology for experimental modal analysis with a little user supervision [14,15].…”