Structural health monitoring (SHM) systems perform automated non-destructive damage
detection and characterization for a variety of large structures including civil structures
such as bridges and aerospace structures such as aircrafts and space vehicles. The
goals of SHM include preventing catastrophic structural failures, increasing reliability, reducing
maintenance costs, and increasing the useful life span of the structures monitored
by the systems.
Guided waves have been extensively studied as means for monitoring the state of
large structures. Guided waves, such as Lamb waves, propagate within the thickness of
the structure, and are sensitive to damage. This makes them attractive for use in SHM
systems. However, they are characterized by complex propagation characteristics such
as dispersion, and multimodal and frequency-dependent attenuation, which often complicate
analysis. In my dissertation research, we developed and evaluated four important
components of a reliable guided wave-based SHM system for aerospace structures made
out of composite materials and metals. These are:
1. A cross Wigner-Ville distribution-based mode decomposition algorithm to separate
overlapped modes in sensor signals. Separating the mode components in sensor
signals has several applications in SHM. Algorithms (2) and (3) are two examples
where separated mode components are used.
2. A sparse tomographic reconstruction algorithm based on decomposed mode components
to estimate the extent of damage on the structure. Estimating the extent
of damage allows us to reliably predict the remaining useful life of the structure.
The anomaly-imaging algorithm estimates damage extent with accuracies comparable
to manual ultrasonic inspection techniques such as C-scan when the sensor
density is sufficiently high.
3. An algorithm to compensate for the effect of temperature on sensor signals. The
damage characterization algorithm developed in (2) requires a set of baseline signals
recorded on the structure before the introduction of damage. Temperature
changes can introduce changes in sensor signals that maybe interpreted as damage.
The temperature compensation algorithm will mitigate difficulties caused by such
changes in sensor signals.
4. A baseline-free damage detection algorithm from sensor signals under varying environmental
conditions. Baseline comparison methods for SHM in time-varying
environments require training on data recorded from damaged structures. The
baseline-free damage detection algorithm overcomes this challenge. The algorithm
is trained using only signals acquired from the damage-free structures.
The four algorithms presented in this dissertation have the potential to form the
basis for the next generation of SHM systems for aerospace structures and provide unprecedented
accuracy in terms of detecting damage and estimating its extent for better
residual structural analysis. Such a system will facilitate safer air travel. In addition,
it will hasten the transition from currently employed schedule-based maintenance to
a condition-based maintenance strategy resulting in less downtime time and reduced
maintenance costs for aerospace structures.