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).
Research is being conducted in damage diagnosis and prognosis to develop state awareness models and residual useful
life estimates of aerospace structures. This work describes a methodology using Support Vector Machines (SVMs),
organized in a binary tree structure to classify the extent of a growing crack in lug joints. A lug joint is a common
aerospace 'hotspot' where fatigue damage is highly probable. The test specimen was instrumented with surface mounted
piezoelectric transducers and then subjected to fatigue load until failure. A Matching Pursuit Decomposition (MPD)
algorithm was used to preprocess the sensor data and extract the input vectors used in classification. The results of this
classification scheme show that this type of architecture works well for categorizing fatigue induced damage (crack) in a
computationally efficient manner. However, due to the nature of the overlap of the collected data patterns, a classifier at
each node in the binary tree is limited by the performance of the classifier that is higher up in the tree.
Fatigue crack growth during the service life of aging aircraft is a critical issue and monitoring of such cracks in structural
hotspots is the goal of this research. This paper presents a procedure for classification and detection of cracks generated
in bolted joints which are used at numerous locations in aircraft structures. Single lap bolted joints were equipped with
surface mounted piezoelectric (pzt) sensors and actuators and were subjected to cyclic loading. Crack length
measurements and sensor data were collected at different number of cycles and with different torque levels. A
classification algorithm based on Support Vector Machines (SVMs) was used to compare signals from a healthy and
damaged joint to classify fatigue damage at the bolts. The algorithm was also used to classify the amount of torque in the
bolt of interest and determine if the level of torque affected the quantification and localization of the crack emanating
from the bolt hole. The results show that it is easier to detect the completely loose bolt but certain changes in torque,
combined with damage, can produce some non-unique classifier solutions.
The present research investigates the complex phenomena of wave scattering in bolted joint. The goal is to develop an understanding of the attenuation behavior of propagating waves, through the structure, as the bolt is subjected to different torques. This is a first step towards developing a structural health monitoring technique for detecting torque loss at a bolted joint. To simulate the local effects of the bolt, a micromechanics based model has been developed to model the scattering and attenuation behavior due to a single fiber in a matrix with a circumferential interface crack. A slicing approach is used to account for the effect of multiple interfacial cracks at different orientations through the depth
of the structure, to simulate the global effects of the bolt. The change in wave attenuation as a function of bolt location, at different depths in a plate, is studied. Next, the variation in wave scattering as the bolt, which is now fully embedded in the plate, is subjected to different torque is investigated. The local stress fields that develop in the plate due to the torque are treated as a pre-stress condition and their effect on the resultant wave scattering is investigated using the developed model. The resultant attenuation accounts for the combined effect of the geometrical attenuation and the attenuation due to the pre-stress. Numerical results obtained show small but steady increase in the attenuation with the applied torque. Experiments conducted to validate the developed model show similar trends.
In structural health monitoring, the fundamental goal is to address the problem of damage identification, localization and quantification. Using the wave based approach, the presence of damage is visualized in terms of the changes in the signature of the resultant wave that propagates through the structure. Since surface mounted piezoelectric transducers have been used for monitoring, the voltage output of each sensor is used for signature characterization. Due to the time-varying nature of these signals, performance of some existing analyzing tools may not be satisfactory. In the present study, the use of the matching pursuit decomposition has been investigated as a signal processing technique to compare signals from healthy and damaged structures.
In structural health monitoring, energy dissipation of wave propagation is a key factor to determine optimal placement of sensors and quantify damage. This paper focuses on the study of wave scattering and attenuation in fiber-reinforced composite laminates with damage. In order to obtain the overall attenuation coefficient, the propagation of elastic shear wave in fiber-matrix medium is investigated starting from the Helmholtz equation. The wave attenuation due to interfacial damage is considered. The attenuation due to cracks of varying sizes and the effect of frequency on the attenuation value has been examined. It can be shown that a critical frequency exists at a given crack size for which the attenuation in the composite medium is at its highest value. Furthermore, the wave attenuation in composite laminates is investigated by incorporating energy transfer in layerwise medium. The overall attenuation coefficient for the laminate is obtained. Experiments are also conducted to evaluate some of the observations obtained from the model.
The goal of this paper is to discuss different data interpretation concepts for structural health monitoring based on Lamb wave propagation. Surface-mounted piezoelectric transducers are used as wave emitters and receivers. The waves emitted interact with discontinuities and experience a change in their propagation characteristics when damage is generated. By comparing sensor signals collected before and after damage has been generated, the condition of the structure can be determined. Two approaches are proposed for characterizing the damage. The initial approach analyzes only the first wave packet reflected at the damage and is based on a simple time-of-flight analysis in conjunction with a geometric method. The size of the damage can be estimated with this technique by using several pairs of piezoelectric conversion processes. This technique offers the potential for enhancing measurement accuracy. An estimation of the size and location of the damage becomes possible with just three piezoelectric sensors. It has been found that, in practice, the superposition of wave packets represents one of the main problems in realizing this approach. The decomposition of superimposed wave packets is investigated using wavelet transform and the pulse compression technique. It is shown that noise and dispersion represent a major drawback, hence rendering signal analysis distinctly more difficult.