The effective detection and classification of damage in complex structures is an important task in the realization
of structural health monitoring (SHM) systems. Conventional information processing techniques utilize statistical
modeling machinery that requires large amounts of 'training' data which is usually difficult to obtain, leading to compromised system performance under these data-scarce conditions. However, in many SHM scenarios a modest amount of data may be available from a few different but related experiments. In this paper, a new structural damage classification method is proposed that makes use of statistics from related task(s) to improve the classification performance on a data set with limited training examples. The approach is based on the framework of transfer learning (TL) which provides a mechanism for information transfer between related
learning tasks. The utility of the proposed method is demonstrated for the classification of fatigue damage in an aluminum lug joint.
Adaptive learning techniques have recently been considered for structural health monitoring applications due
to their flexibility and effectiveness in addressing real-world challenges such as variability in the monitoring of
environmental and operating conditions. In this paper, an active learning data selection procedure is proposed
that adaptively selects the most informative measurements to include, from multiple available measurements, in
estimating structural damage. This is important, since not all the measurements may provide useful information
and could reduce performance when processed. Within the adaptive learning framework, the data selection
problem is formulated to choose those measurements which are most representative of the diversity within a
damage state. This is achieved by extracting time-frequency analysis based statistical similarity features from
the measurements, and selecting uniformly distributed subsets to build representative reference sets. The utility
of the proposed method is demonstrated by improvements in adaptive learning performance for the estimation
of fatigue damage in an aluminum compact tension sample.
Fatigue damage sensing and measurement in aluminum alloys is critical to estimating the residual useful lifetime of a
range of aircraft structural components. In this work, we present electrical impedance and ultrasonic measurements in
aluminum alloy 2024 that has been fatigued under high cycle conditions. While ultrasonic measurements can indicate
fatigue-induced damage through changes in stiffness, the primary indicator is ultrasonic attenuation. We have used laser
ultrasonic methods to investigate changes in ultrasonic attenuation since simultaneous measurement of longitudinal and
shear properties provides opportunities to develop classification algorithms that can estimate the degree of damage.
Electrical impedance measurements are sensitive to changes in the conductivity and permittivity of materials - both are
affected by the microstructural damage processes related to fatigue. By employing spectral analysis of impedance over a
range of frequencies, resonance peaks can be identified that directly reflect the damage state in the material. In order to
compare the impedance and ultrasonic measurements for samples subjected to tension testing, we use processing and
classification tools that are matched to the time-varying spectral nature of the measurements. Specifically, we process
the measurements to extract time-frequency features and estimate stochastic variation properties to be used in robust
classification algorithms. Results are presented for fatigue damage identification in aluminum lug joint specimens.
We have recently proposed a method for classifying waveforms from healthy and damaged structures in a structural
health monitoring framework. This method is based on the use of hidden Markov models with preselected
feature vectors obtained from the time-frequency based matching pursuit decomposition. In order to investigate
the performance of the classifier for different signal-to-noise ratios (SNR), we simulate the response of a lug joint
sample with different crack lengths using finite element modeling (FEM). Unlike experimental noisy data, the
modeled data is noise free. As a result, different levels of noise can be added to the modeled data in order to
obtain the true performance of the classifier under additive white Gaussian noise. We use the finite element
package ABAQUS to simulate a lug joint sample with different crack lengths and piezoelectric sensor signals.
A mesoscale internal state variable damage model defines the progressive damage and is incorporated in the
macroscale model. We furthermore use a hybrid method (boundary element-finite element method) to model
wave reflection as well as mode conversion of the Lamb waves from the free edges and scattering of the waves
from the internal defects. The hybrid method simplifies the modeling problem and provides better performance
in the analysis of high stress gradient problems.
We investigate the use of low frequency (10-70 MHz) laser ultrasound for the detection of fatigue damage.
While high frequency ultrasonics have been utilized in earlier work, unlike contacting transducers, laser-based
techniques allow for simultaneous interrogation of the longitudinal and shear moduli of the fatigued material. The
differential attenuation changes with the degree of damage, indicating the presence of plasticity. In this paper, we
describe a structural damage identification approach based on ultrasonic sensing and time-frequency techniques.
A parsimonious representation is first constructed for the ultrasonic signals using the modified matching pursuit
decomposition (MMPD) method. This decomposition is then employed to compute projections onto the various
damage classes, and classification is performed based on the magnitude of these projections. Results are presented
for the detection of fatigue damage in Al-6061 and Al-2024 plates tested under 3-point bending.