  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.
