We describe a stochastic ltering approach for tracking progressive fatigue damage in structures, wherein physically based damage evolution information is combined with active sensing guided wave measurements. The input waveform used to excite dispersive modes within the structure is adaptively con gured at each time step in order to maximize the damage estimation performance. The damage evolution model is based on Paris Law, and hidden Markov modeling of time-frequency features obtained from received signals is used to de ne the measurement model. Damage state estimation is performed using a particle lter. Results are presented for fatigue crack estimation in an aluminum specimen.
The challenge of rapid footstep detection and classification in remote locations has long been an important area of study for defense technology and national security. Also, as the military seeks to create effective and disposable unattended ground sensors (UGS), computational complexity and power consumption have become essential considerations in the development of classification techniques. In response to these issues, a research project at the Flexible Display Center at Arizona State University (ASU) has experimented with footstep classification using the matching pursuit decomposition (MPD) time-frequency analysis method. The MPD provides a parsimonious signal representation by iteratively selecting matched signal components from a pre-determined dictionary. The resulting time-frequency representation of the decomposed signal provides distinctive features for different types of footsteps, including footsteps during walking or running activities. The MPD features were used in a Bayesian classification method to successfully distinguish between the different activities. The computational cost of the iterative MPD algorithm was reduced, without significant loss in performance, using a modified MPD with a dictionary consisting of signals matched to cadence temporal gait patterns obtained from real seismic measurements. The classification results were demonstrated with real data from footsteps under various conditions recorded using a low-cost seismic sensor.
This paper examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage
in a complex metallic structural component in the presence of temperature variations. The goal of this research is to
improve damage localization results for a structural component interrogated at an unknown temperature by developing a
probabilistic and reference-free framework for estimating Lamb wave velocities. The proposed approach for
temperature-independent damage localization involves a model that can describe the change in Lamb wave velocities
with temperature, the use of advanced time-frequency based signal processing for damage feature extraction, estimation
of the actual Lamb wave velocities from transducer signals, and a Bayesian damage localization framework with data
association and sensor fusion. The technique does not require any additional transducers on a component and allows the
estimation of the velocities for the actual Lamb waves present in a component. Experiments to validate the proposed
method were conducted using an aluminum lug joint interrogated with piezoelectric transducers for a range of
temperatures and fatigue crack lengths. Experimental results show the advantages of using a velocity estimation
algorithm to improve damage localization for a component interrogated at both known and unknown temperatures.
Noise and interference in sensor measurements degrade the quality of data and have a negative impact on the
performance of structural damage diagnosis systems. In this paper, a novel adaptive measurement screening
approach is presented to automatically select the most informative measurements and use them intelligently for
structural damage estimation. The method is implemented efficiently in a sequential Monte Carlo (SMC) setting
using particle filtering. The noise suppression and improved damage estimation capability of the proposed method
is demonstrated by an application to the problem of estimating progressive fatigue damage in an aluminum
compact-tension (CT) sample using noisy PZT sensor measurements.
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.
We propose a sequential Monte Carlo (SMC) based progressive structural damage diagnosis framework that
tracks damage by integrating information from physics-based damage evolution models and using stochastic
relationships between the measurements and the damage. The approach described in this paper adaptively
configures the sensors used to collect the measurements using the minimum predicted mean squared error (MSE)
as the performance metric. Optimization is performed globally over the entire search space of all available
sensors. Results are presented for the diagnosis of fatigue damage in a notched laminate, demonstrating the
effectiveness of the proposed method.
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 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.
We describe a statistical method for the classification of damage in complex structures. Our approach is based
on a Bayesian framework using hidden Markov models (HMMs) to model time-frequency features extracted from
structural data. We also propose two different methods for sensor fusion to combine information from multiple
distributed sensors such that the overall classification performance is increased. The proposed approaches are
applied to the classification and localization of delamination in a laminated composite plate. Results using
both discrete and continuous observation density HMMs, together with the sensor fusion, are presented and
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.
The ability to detect and classify damages in complex materials and structures is an important problem from
both safety and economical perspectives. This paper develops a novel approach based on Hidden Markov Models
(HMMs) for the classification of structural damage. Our approach here is based on using HMMs for modeling
the time-frequency features extracted from time-varying structural data. Unlike conventional deterministic
methods, the HMM is a stochastic approach which better accounts for the uncertainties encountered in the
structural problem and leads to a more robust health monitoring system. The utility of the proposed approach
is demonstrated via example results for the classification of fastener damage in an aluminum plate.