This work focuses on fatigue crack detection, crack tip localization and quantification in plate like structures
using a reference-free method. In many practical applications the environmental conditions in which a structure
is operated do not remain same over time. Sensor signals, thus, collected for the damaged state cannot be
compared directly with the baseline because a change in the signal can be caused by several factors other than a
structural damage. Therefore, reference-free methods are needed for damage detection. Two methods have been
discussed in this paper, one with collocated sensors and the other using matching pursuit decomposition (MPD)
to detect waves undergoing mode conversion from fatigue crack tip. The time of flight (TOF) of these mode
converted waves along with their respective velocities are further used to localize the crack tip and ultimately find
the extent of crack. Both these approaches were used to detect fatigue cracks in aluminum plates made of 6061
alloy. These samples were instrumented with collocated piezoelectric sensors and tested under constant amplitude
fatigue loading. Crack tip localization was done from the TOF information extracted for mode converted waves
using MPD. The crack lengths obtained using this reference-free technique were validated with experimental crack lengths and were found to be in good agreement.
A procedure to monitor crack growth in Aluminum lug joints subject to fatigue loading is developed. Sensitivity
analysis is used to decide sensor importance and monitor crack growth rate. A new feature extraction technique
based on Discrete Cosine Transformation (DCT) is developed to analyze complex sensor signals. Self-sensing
piezoelectric sensors are surface mounted on Al 2024 T351 lug joint samples, 0.25 in. thickness. Samples with
single crack site and multiple crack sites were used in this study and to initiate multiple crack sites, they were
notched symmetrically near the shoulders and then tested under a fatigue load of 110lbs (0.49kN) to 1100lbs
(4.9kN). Crack lengths were monitored over the entire life of the lug joint sample using a CCD camera. Active
sensing was carried out at every crack length, when the machined was stopped. The piezoelectric actuator was
excited with a chirp signal, swept between 1kHz to 500kHz, and sensor readings were collected at a sampling rate
of 2Ms/s. Using three different sensor sensitivity algorithms, the sensor signals are analyzed and their efficiency
in predicting crack growth rates and deciding sensor importance is studied. Sensor sensitivity is defined as the
changes observed in sensor signals obtained from a damaged sample compared to healthy sample. The first
two algorithms, ORCA and One-Class SVM's, are based on statistical techniques for outlier detection and the
third algorithm, a new detection framework, is based on feature extraction using Discrete Cosine Transformation
(DCT). The efficacy of these methods for damage characterization is presented.
This paper presents the use of guided wave concept in localizing small cracks in complex lug joint structures. A lug joint
is a one of the several 'hotspots' in an aerospace structure which experiences fatigue damage. Several fatigue tests on lug
joint samples prepared from 0.25" plate of Aluminum (Al) 2024 T351 indicated a distinct failure pattern. All samples
failed at the shoulders. Different notch sizes are introduced at the shoulders and both virtual and real active health
monitoring with piezoelectric transducers is performed. Simulations of the real time experiment are carried out using
Finite Element (FE) analysis. Similar crack geometry and piezoelectric transducer orientation are considered both in
experiment and in simulation. Results presented illustrate the use of guided waves in interrogating damage in lug joints.
A comparison of sensor signals has been made between experimental and simulated signals which show good
correlation. The frequency transform on the sensor signal data yield useful information for characterizing damage.
Further, sensitivity studies are performed. The sensitivity study information offers potential application in reducing the
computational cost for any defect localization technique by reducing redundant sensors. This information is a key to
optimal sensor placement for damage detection in structural health monitoring (SHM).
KEYWORDS: Signal to noise ratio, Data modeling, Time-frequency analysis, 3D modeling, Interference (communication), Finite element methods, Physics, Chemical species, Performance modeling, Sensors
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.