Environmental Engineering Reference
In-Depth Information
detection method based on an analysis of the low-frequency structural vibration
and combined the local damage detection methods. In their works, the modal
identification was implemented by means of the so-called operational modal
analysis methods [
15
].
For vibration-based damage detection methodologies, the initial assumption is
that the structural vibration properties are affected only by structural damage,
which results in changes in the structural stiffness and/or structural mass. It has
been proven that vibration-based methodologies are valid for most mechanical and
aerospace structures. However, more than 10 years ago, in the civil engineering
community, it was found that for real full-scale engineering structures, varying
environmental factors such as temperature, prestress, traffic load, wind, and
humidity, can also affect the structural vibration properties. These environmental
factors may mask the changes caused by structural damage [
21
-
30
]. If the effect of
these environmental or operational variations is not taken into account in the
damage detection process, a false-positive or negative damage diagnosis may
occur such that vibration-based damage detection becomes unreliable.
For a wind turbine blade in operational condition, uniform temperature effects
can be ignored because it is a cantilever beam structure, and gradient temperature
effects will be small due to its continuous motion and relatively small section size.
However, rotational motion interferes with the detection of damage in a wind
turbine blade because it changes the vibrational characteristics of the blade.
Compared to nonrotating structures, the stiffness of the wind turbine blades will
increase due to both stretching caused by centrifugal inertia forces caused by the
rotational motion and the increment of the bending stiffness of the structure, which
results in the variation of the natural frequencies and mode shapes [
31
-
34
].
Osgood [
33
] and Park et al. [
34
] demonstrated that the natural frequencies of wind
turbine blades may change with their rotational speed. In this case, vibration-based
damage detection methods will be invalid because the structural modal properties
vary with the rotational speed of wind turbine blades and not only due to structural
damage.
Furthermore, it is well known that all vibration-based damage detection pro-
cesses rely on vibration data with inherent uncertainties, which are due to the
mechanical model, the data acquisition system, and other process noise; thus, it
makes sense to use statistical methods to handle damage problems. A few methods
of this type have been developed [
35
-
38
]. The purpose of the current study is to
make use of a methodology based on principal component analysis (PCA), which
is able to reject the rotational effect to detect damage using vibration data from a
healthy and damaged structure. This method was proposed by Yan et al. for
structural damage diagnosis under varying environmental conditions [
23
]. In this
method, the influencing variables need not be measured in advance, and their
effects will be removed during the damage detection procedure. The remaining
minor components were used to detect the damage.
This chapter descripts structural health monitoring of wind turbine blades using
three frequently used techniques including PZT-based, vibration-based, and opti-
cal sensor-based damage detection methods.
