With research in structural health monitoring (SHM) moving towards increasingly complex structures for damage
interrogation, the placement of sensors is becoming a key issue in the performance of the damage detection
methodologies. For ultrasonic wave based approaches, this is especially important because of the sensitivity of the
travelling Lamb waves to material properties, geometry and boundary conditions that may obscure the presence of
damage if they are not taken into account during sensor placement. The framework proposed in this paper defines a
sensing region for a pair of piezoelectric transducers in a pitch-catch damage detection approach by taking into account
the material attenuation and probability of false alarm. Using information about the region interrogated by a sensoractuator
pair, a simulated annealing optimization framework was implemented in order to place sensors on complex
metallic geometries such that a selected minimum damage type and size could be detected with an acceptable probability
of false alarm anywhere on the structure. This approach was demonstrated on a lug joint to detect a crack and on a large
Naval SHM test bed and resulted in a placement of sensors that was able to interrogate all parts of the structure using the minimum number of transducers.
Proc. SPIE. 7649, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2010
KEYWORDS: Carbon, Aerospace engineering, Fiber Bragg gratings, Sensors, Composites, Nondestructive evaluation, Computer simulations, Structural health monitoring, Microsoft Foundation Class Library, Algorithm development
Localization of low energy impacts on carbon fiber composites is an important aspect of structural health monitoring
since it creates subsurface damage which can significantly reduce the stiffness of a component. A novel impact
localization method is proposed based on the strain amplitude measured by Fiber Bragg Grating (FBG) sensors. The
algorithm is based on the relative placement of all sensors and the maximum strain amplitude measured by each sensor.
This method requires minimal knowledge of the material or the structure and a minimum number of sensors. The
algorithm showed good results on both simulated and experimental test cases of woven composite plates. It was found
that a minimum of five FBG are necessary to accurately predict the impact location on a plate. The algorithm was also
tested on a woven composite wing showing good localization along the span of the wing but higher errors along the chord length due to the nonlinearity in the measured strains.
Automated detection of damage due to impact in composite structures is very important for aerospace structural health
monitoring (SHM) applications. Fiber Bragg grating (FBG) sensors show promise in aerospace applications since they
are immune to electromagnetic interference and can support multiple sensors in a single fiber. However, since they only
measure strain along the length of the fiber, a prediction scheme that can estimate loading using randomly oriented
sensors is key to damage state awareness. This paper focuses on the prediction of impact loading in composite structures
as a function of time using a support vector regression (SVR) approach. A time delay embedding feature extraction
scheme is used since it can characterize the dynamics of the impact using the sensor signal from the FBGs. The
efficiency of this approach has been demonstrated on simulated composite plates and wing structures. Training with
impacts at four locations with three different energies, the constructed framework is able to predict the force-time history
at an unknown impact location to within 12 percent on the composite plate and to within 10 percent on a composite wing when the impact was within the sensor network region.
The ability to detect anomalies in signals from sensors is imperative for structural health monitoring (SHM) applications.
Many of the candidate algorithms for these applications either require a lot of training examples or are very
computationally inefficient for large sample sizes. The damage detection framework presented in this paper uses a
combination of Linear Discriminant Analysis (LDA) along with Support Vector Machines (SVM) to obtain a
computationally efficient classification scheme for rapid damage state determination. LDA was used for feature
extraction of damage signals from piezoelectric sensors on a composite plate and these features were used to train the
SVM algorithm in parts, reducing the computational intensity associated with the quadratic optimization problem that
needs to be solved during training. SVM classifiers were organized into a binary tree structure to speed up classification,
which also reduces the total training time required. This framework was validated on composite plates that were
impacted at various locations. The results show that the algorithm was able to correctly predict the different impact
damage cases in composite laminates using less than 21 percent of the total available training data after data reduction.
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.