Vibration-Based Damage Detection in Wind Turbine Blades using Phase-Based Motion Estimation and Motion Magnification (1804.00558v1)

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-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.

• The paper demonstrates that phase-based motion estimation combined with motion magnification detects blade damage with less than 0.6 Hz discrepancy from conventional methods.
• The method extracts operational deflection shapes and resonant frequencies from high-resolution video, eliminating the need for sensor-induced mass loading.
• The approach overcomes traditional sensor limitations, paving the way for automated, non-intrusive inspections in wind turbine structural health monitoring.

Vibration-Based Damage Detection in Wind Turbine Blades

The research paper, "Vibration-Based Damage Detection in Wind Turbine Blades using Phase-Based Motion Estimation and Motion Magnification," presents an advanced, non-contact methodology for structural damage detection in wind turbine blades using innovative computer vision techniques. The authors introduce a method that leverages Phase-based Motion Estimation (PME) coupled with motion magnification, aiming to overcome the limitations associated with traditional vibration-based Structural Health Monitoring (SHM) approaches that typically rely on accelerometers and strain sensors.

Core Methodology and Numerical Outcomes

The primary method involves extracting motion from sequences of images using PME, thus eliminating the need for physical sensors that induce mass loading effects on the structure. A high-resolution digital video camera captures the wind turbine blade's vibrations, and the PME processes these images to determine the operational deflection shapes (ODS) and resonant frequencies. This paper's experimental setup includes examining a 2.3-meter long Skystream® wind turbine blade during baseline and externally mass-loaded (simulated damage) conditions.

The results demonstrate alignment between resonant frequencies obtained from conventional Experimental Modal Analysis (EMA) with accelerometers and those extracted from the novel non-contact PME approach, achieving discrepancies of less than 0.6 Hz. This validation is crucial as it establishes PME as a viable alternative to EMA, potentially offering a more practical and efficient solution for monitoring large-scale structures. Furthermore, applying damage to the wind turbine blade by increasing mass yields noticeable changes in its modal parameters, thereby confirming PME's efficacy in detecting structural alterations indicative of damage.

Technical Implications and Future Prospects

This paper showcases the potential of using PME and motion magnification for SHM applications, offering several advantages, including the absence of mass loading effects and the capability for full-field spatial measurements without complex sensor arrays. While the process is currently limited to capturing two-dimensional motion, advancements in multi-camera setups could extend this ability to three-dimensional space, broadening the applicability of PME in diverse structural contexts.

Notably, improving PME's automation, particularly in motion magnification and edge detection computation, remains a priority. Addressing the computational constraints associated with real-time video processing and enhancing the extraction of Operating Deflection Shapes (ODS) will be integral to future implementations. The successful adaptation of these methodologies into automated systems would facilitate routine inspections of wind turbine blades, potentially inoperational, by reducing the dependency on manual analysis and enhancing predictive maintenance strategies.

Conclusion and Theoretical Implications

The application of PME in identifying structural damage substantiates its potential as a pivotal tool in the field of SHM. By circumventing the drawbacks of existing measurement techniques, this approach offers a promising pathway for efficiently assessing the structural integrity of complex structures. The findings suggest a fundamental shift towards more adaptive, accurate, and less intrusive methods of damage detection, which could redefine standard practices across multiple engineering domains concerned with structural health and safety.

Future research will likely explore PME's sensitivity to various damage types beyond mass addition, collaborating with machine learning approaches to refine damage localization and diagnosis precision. Understanding the full potential of PME in SHM not only has practical significance but also contributes to the broader theoretical discourse on computational imaging and structural dynamics.