-
Sequential Multiple Structural Damage Detection and Localization: A Distributed Approach
As essential components of the modern urban system, the health condition...
read it
-
Damage detection in operational wind turbine blades using a new approach based on machine learning
The application of reliable structural health monitoring (SHM) technolog...
read it
-
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications
Monitoring structural damage is extremely important for sustaining and p...
read it
-
Multi-point Vibration Measurement for Mode Identification of Bridge Structures using Video-based Motion Magnification
Image-based vibration mode identification gained increased attentions in...
read it
-
On the Potenital of Dynamic Substructuring Methods for Model Updating
While purely data-driven assessment is feasible for the first levels of ...
read it
-
A Multi-Objective DIRECT Algorithm Towards Structural Damage Identification with Limited Dynamic Response Information
A major challenge in Structural Health Monitoring (SHM) is to accurately...
read it
-
Hierarchical sparse Bayesian learning: theory and application for inferring structural damage from incomplete modal data
Structural damage due to excessive loading or environmental degradation ...
read it
Vibration-Based Damage Detection in Wind Turbine Blades using Phase-Based Motion Estimation and Motion Magnification
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.
READ FULL TEXT
Comments
There are no comments yet.