Proc. SPIE. 7295, Health Monitoring of Structural and Biological Systems 2009
KEYWORDS: Databases, Composites, Feature extraction, Ultrasonics, Signal processing, Structural health monitoring, Damage detection, Microsoft Foundation Class Library, Adhesives, Autoregressive models
Ultrasonic chaotic excitations combined with sensor prediction algorithms have shown the ability to identify incipient
damage (loss of preload) in a bolted joint. In this study we examine the capability of this damage detection scheme to
identify disbonds and poorly cured bonds in a composite-to-composite adhesive joint. The test structure consists of a
carbon fiber reinforced polymer (CFRP) plate that has been bonded to a CFRP rectangular tube/spar with several sizes of
disbond as well as a poorly cured section. Each excitation signal is imparted to the CFRP plate through a macro-fiber
composite (MFC) patch on one side of the adhesive joint and sensed using an equivalent MFC patch on the opposite side
of the joint. A novel statistical classification feature is developed from information theory concepts of cross-prediction
and interdependence. Temperature dependence of this newly developed feature will also be examined.
Proc. SPIE. 6935, Health Monitoring of Structural and Biological Systems 2008
KEYWORDS: Data modeling, Sensors, Satellites, Ultrasonics, Signal processing, Structural health monitoring, Microsoft Foundation Class Library, Algorithm development, Autoregressive models, Performance modeling
The Operationally Responsive Space (ORS) strategy hinges, in part, on realizing technologies which can facilitate
the rapid deployment of satellites. Presently, preflight qualification testing and vehicle integration processes
are time consumptive and pose as two significant hurdles which must be overcome to effectively enhance US
space asset deployment responsiveness. There is a growing demand for innovative embedded Structural Health
Monitoring (SHM) technologies which can be seamlessly incorporated onto payload hardware and function in
parallel with satellite construction to mitigate lengthy preflight checkout procedures. In this effort our work is
focused on the development of a joint connectivity monitoring algorithm which can detect, locate, and assess
preload in bolted joint assemblies. Our technology leverages inexpensive, lightweight, flexible thin-film macro-fiber composite (MFC) sensor/actuators with a novel online, data-driven signal processing algorithm. This
algorithm inherently relies upon Chaotic Guided Ultrasonic Waves (CGUW) and a novel cross-prediction error
classification technique. The efficacy of the monitoring algorithm is evaluated through a series of numerical
simulations and experimentally in two test configurations. We conclude with a discussion surrounding further
development of this approach into a commercial product as a real-time flight readiness indicator.
Proc. SPIE. 6935, Health Monitoring of Structural and Biological Systems 2008
KEYWORDS: Detection and tracking algorithms, Modulation, Waveguides, Feature extraction, Ultrasonics, Structural health monitoring, Damage detection, Microsoft Foundation Class Library, Autoregressive models, Evolutionary algorithms
Recent research has shown that chaotic structural excitation and state space reconstruction may be used beneficially
in structural health monitoring (SHM) processes. This relationship has been exploited for use in detection of bolt
preload reduction by using a chaotic waveform with ultrasonic frequency content with a damage detection algorithm
based on auto-regressive (AR) modeling. The signal is actively applied to a structure using a bonded macro fiber
composite (MFC) patch. The response generated by the mechanical interaction of the MFC patch with the structure is
then measured by other affixed MFC patches. In this study the suitability of particular chaotic waveforms will be
investigated through the use of evolutionary algorithms. These algorithms are able to find an optimum excitation for
maximum damage state discernability whose fitness is two orders of magnitude greater than choosing random
parameters for signal creation.
Proc. SPIE. 6174, Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
KEYWORDS: Actuators, Data modeling, Waveguides, Signal attenuation, Pattern recognition, Error analysis, Structural health monitoring, Microsoft Foundation Class Library, Autoregressive models, Performance modeling
Recent research has shown that high frequency chaotic excitation and state space reconstruction may be used to identify incipient damage (loss of preload) in a bolted joint. In this study, a new experiment is undertaken with updated test equipment, including a piezostack actuator that allows for precise control of bolt preload. The excitation waveform is applied to a macro-fiber composite (MFC) patch that is bonded to the test structure and is sensed in an active manner using a second MFC patch. A novel prediction error algorithm, based on comparing filtered properties of the guided chaotic waves, is used to determine the damage state of a frame structure and is shown to be highly sensitive to small levels of bolt preload loss. The performance of the prediction error method is compared with standard structural health monitoring damage features that are based on time series analysis using auto-regressive (AR) models.
Lamb waves have been widely used as an efficient means of detecting damage in plate-like structures.
Numerous signal-processing techniques are available for evaluating and processing measured Lamb waves for damage
identification. In this study, we investigated the use of a novel excitation that is created by frequency up-conversion of a
standard Lorenz chaotic signal into the acoustic regime. Recent research has shown that high frequency chaotic
excitation and state space reconstruction may be used to identify incipient damage (loss of preload) in a bolted joint. In
this study, an experiment is undertaken using an aluminum plate with an array of piezoceramic patches bonded to one
side. Damage is initiated through the process of electrolytic corrosion. A novel spatio-temporal prediction error
algorithm is used to determine the existence, location, and extent of damage. This paper summarizes considerations
needed to design such a damage identification system, experimental procedures and results, and additional issues that
can be used as a guideline for future investigation.
Proc. SPIE. 5768, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems IV
KEYWORDS: Signal to noise ratio, Ferroelectric materials, Waveguides, Pattern recognition, Error analysis, Amplifiers, Data acquisition, Structural health monitoring, Reconstruction algorithms, Active remote sensing
Recent research has shown that chaotic structural excitation and state space reconstruction may be used beneficially in structural health monitoring (SHM). The focus of this study is to apply a chaotic waveform to a piezoelectric (PZT) patch that is bonded to a test structure. The use of high frequency chaotic excitation (~80 kHz) combined with PZT active sensing allows the location of incipient damage in the structure to be easily identified. The generation of high frequency chaos from a low frequency process necessitates bandwidth up-conversion that has been shown to be dimension preserving. We investigate the use of this method in conjunction with a novel prediction error algorithm to determine the damage state of a frame structure.
In this study, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is explored. Damage sensitive features that explicitly consider the nonlinear system input/output relationships produced by damage are extracted from the ARX model. Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS is useful in this case because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to nonlinear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three-story building model. It is found that the current method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance-based method using piezoelectric (PZT) material as both an actuator and a sensor is then proposed as a possible solution to the problem of damage localization.