This paper presents a preliminary study of the effects residual plastic strains have on Lamb wave velocities and time of flight measurements. The potential application of this research is non-destructive evaluation and structural health monitoring, particularly reconstructing plastic strain fields. The finite deformation of a semi-infinite plate due to residual plastic strain is used to accommodate the changes in plate thickness and elongation. The results show that the S0 mode exhibits significant variations in group velocity in the highly dispersive regions, as much as a 2% increase in velocity with a 1% plastic strain. However, for time of flight measurements, the plate elongation had an order of magnitude effect rather than showing velocity changes. By exploiting time delay measurements, it may be possible to use wave speed measurements to determine plastic zones through Lamb-like waves.
The primary goal of Army Prognostics & Diagnostics is to develop real-time state awareness technologies for primary
structural components. In fatigue-critical structural maintenance, the probabilistic structural risk assessment (PSRA)
methodology for fatigue life management using conventional nondestructive investigation (NDI) has been developed
based on the assumption of independent inspection outcomes. When using the emerging structural health monitoring
(SHM) systems with in situ sensors, however, the independent assumption no longer holds, and the existing PSRA
methodology must be modified. The major issues currently under investigation are how to properly address the
correlated inspection outcomes from the same sensors on the same components and how to quantify its effect in the
SHM-based PSRA framework. This paper describes a new SHM-based PSRA framework with a proper modeling of
correlations among multiple inspection outcomes of the same structural component. The framework and the associated
probabilistic algorithms are based on the principles of fatigue damage progression, NDI reliability assessment and
structural reliability methods. The core of this framework is an innovative, computationally efficient, probabilistic
method RPI (Recursive Probability Integration) for damage tolerance and risk-based maintenance planning. RPI can
incorporate a wide range of uncertainties including material properties, repair quality, crack growth related parameters,
loads, and probability of detection. The RPI algorithm for SHM application is derived in detail. The effects of
correlation strength and inspection frequency on the overall probability of missing all detections are also studied and
discussed.
There are emerging sensor technologies that will be deployed in future rotorcraft or retrofitted to existing rotorcraft and
aircraft for structural diagnostics and prognostics. The vehicle health management system is likely to contain heterogeneous sensor
arrays. Thus the structural state awareness may require information data fusion from dissimilar sensor (heterogeneous) system. This
paper reviews the state of the art commercial of the shelf (COTS) and emerging sensor technologies for structural damage monitoring
of rotorcraft and aircraft health.
Embedded sensors are used in layers of composite structures to provide local damage detection. The presence
of these sensors causes material and geometric discontinuities which in turn causes unwanted peaks of stress
and strain with consequences on stiffness reduction. Often several of these sensors are embedded in structures
aggregating the adverse effects of discontinuities to degrade the structural integrity. Structural damage is a sparse
phenomenon and the mechanical metrics are smooth functions with few spikes near the location of damage. This
sparsity and spikiness can be exploited to reduce the number of embedded sensors in composite structures. The
goal of this paper is to adapt the compressed sensing theory and detect damage using far fewer sensors than
conventionally possible. To demonstrate the efficacy of our approach, we performed a numerical experiment on a
rectangular plate with a center hole, and have shown that the 2D strain-field can be recovered from few samples
of discrete strain measurements acquired by embedded sensors.
This paper investigates the applications of the piezoelectric acoustic sensors for
real time detection of crack initiation and propagation in ceramic composites. In the first
case this paper presents a smart method to detect and track the ceramic cell crack
initiation and propagating in real time when the solid oxide fuel cell (SOFC) system is in
operation with extremely high temperature (>750 deg. C). The main sources of fracture
and delamination are the ceramic cell interlayers and interfaces during high temperature
thermocycling. This research work is to successfully discern the damages during
assembly, initial damages at first thermocycle and damage propagation, if any, at later
cycles. This paper demonstrates that the AE signals generated from cell cracking and
fracture in the stack modules can be successfully captured by commercially off the shelf
(COTS) AE experimental hardware. The distinguishing characteristics of the cell crack
AE signal and the metal impact AE signals will be presented in the paper. The second
case successfully demonstrates the application of acoustic emission sensors to detect the
first crack initiated due to crushing load on ceramic balls. A comprehensive strategy to
capture the crushing load with respect to first crack initiation has been developed and
would be presented in this paper. The crack is initiated when the Hertzian contact
stresses are higher than the crushing load limit. Some initial results on signal processing
to distinguish between first and second crack initiation would be presented.
Presently there exists no way for direct measurement of strain at high temperature in engine components such as combustion chamber, exhaust nozzle, propellant lines and turbine blades and shaft. Existing fatigue and life prediction studies for high temperature zones in propulsion systems depend on strain/stress values computed from indirect measurements of temperature, flow velocity, pressure, et al. Thermomechanical fatigue (TMF) prediction, which is a critical element for blade design, is a strong function of the temperature and strain profiles. Major uncertainties arise from the inability of current instrumentation to measure temperature and strain at the critical locations. This prevents the structural designer from optimizing the blade design high temperature environment, which is a significant challenging problem in engine design. Ability to measure directly strains in different high temperature zones would deeply enhance the effectiveness of aircraft propulsion systems for fatigue damage assessment and life prediction. State of the art for harsh environment high temperature sensors has improved considerably for the past few years. This paper lays down specifications for high temperature sensors and does the technological assessment of these new sensing technologies. This paper presents a review of the recent advances made in harsh environment sensing systems and takes a peek at the future of such technologies.
KEYWORDS: Sensors, Composites, Neurons, Data acquisition, Analog electronics, Wave propagation, Diagnostics, Structural health monitoring, Signal processing, Prototyping
A small size prototype of a Structural Neural System (SNS) was tested in real time for damage detection in a
laboratory setting and the results are presented in this paper. The SNS is a passive online structural health
monitoring (SHM) system that can detect small propagating damages in real time before the overall failure of the
structure is realized. The passive SHM method is based on the concept of detecting acoustic emissions (AE) due to
damage propagating. Propagating cracks were identified near the vicinity of a sensor in a composite specimen
during fatigue testing. In the composite specimen, in additions to a propagating crack, fretting occurred because of
slipping contact between the load points and the composite specimen. The SNS was able to predict the location of
damage due to crack propagation and also detect signals from fretting simultaneously in real time.
The transient response of delaminated smart composite laminates is studied using an improved layerwise laminate theory. The theory is capable of capturing interlaminar shear stresses that are critical to delamination. The Fermi-Dirac distribution function is combined with an improved layerwise laminate theory to model a smooth transition in the displacement and the strain fields of the delaminated interfaces during “breathing” of delaminated layers. Stress free boundary conditions are enforced at all free surfaces. Continuity in displacement field and transverse shear stresses are enforced at each ply level. In modeling piezoelectric composite plates, a coupled piezoelectric-mechanical formulation is used in the development of the constitutive equations. Numerical analysis is conducted to investigate the effect of nonlinearity in the transient vibration of bimodular behavior caused by the contact impact of delaminated interfaces. Composite plate with surface-bonded or embedded sensors, subject to external loads, are also investigated to study the effects on transient responses due to various sizes and locations of delamination.
Structural Health Monitoring ideally would check the health of the structure in real time all the time. Simplifying the sensor system and the data acquisition equipment plays a very important role in achieving this goal. This paper discusses a practical technique that uses long continuous sensors and biomimetic signal processing to simplify health monitoring. The testing of a structural neural system with an updated analog processor module is discussed in this paper. A neuron is formed by connecting sensor elements to an analog processor. The structural neural system is formed by connecting multiple neurons to mimic the signal processing architecture of the neural system of the human body. This approach reduces the required number of data acquisition channels and still predicts the location of damage within a grid of miniature neurons. Different types of sensors can also be used. A piezoelectric ribbon sensor can sense damage due to impacts or crack growth because these damages generate Lamb waves that are detected by the neural system. The neuron can also receive diagnostic waves generated to check the structure on demand and when it is not in operation. In addition, new continuous multi-wall carbon nanotube sensors are being used as strain and crack detection neurons that operate during both static and dynamic loading. In general, the Structural Neural System may provide an advantage for the continuous monitoring of most large sensor systems in which anomalous events must be detected, and where it is impractical to have a separate channel of data acquisition for each sensor. Moreover, the data reduction technique and damage detection algorithm are easy to understand, simple to implement, reliable, and many sensor types can be used.
Detecting and locating cracks in structural components and joints that have high feature densities is a challenging problem in the field of Structural Health Monitoring. There have been advances in piezoelectric sensors, actuators, wave propagation, MEMS, and optical fiber sensors. However, few sensor-signal processing techniques have been applied to the monitoring of joints and complex structural geometries. This is in part because maintaining and analyzing a large amount of data obtained from a large number of sensors that may be needed to monitor joints for cracks is difficult. Reliable low cost assessment of the health of structures is crucial to maintain operational availability and productivity, reduce maintenance cost, and prevent catastrophic failure of large structures such as wind turbines, aircraft, and civil infrastructure. Recently, there have also been advances in development of simple passive techniques for health monitoring including a technique based on mimicking the biological neural system using electronic logic circuits. This technique aids in reducing the required number of data acquisition channels by a factor of ten or more and is able to predict the location of a crack within a rectangular grid or within an arbitrarily arranged network of continuous sensors or neurons. The current paper shows results obtained by implementing this method on an aluminum plate and joint. The plates were tested using simulated acoustic emissions and also loading via an MTS machine. The testing indicates that the neural system can monitor complex joints and detect acoustic emissions due to propagating cracks. High sensitivity of the neural system is needed, and further sensor development and testing on different types of joints is required. Also indicated is that sensor geometry, sensor location, signal filtering, and logic parameters of the neural system will be specific to the particular type of joint (material, thickness, geometry) being monitored. Also, a novel piezoresistive carbon nanotube nerve crack sensor is presented that can become a neuron and respond to local crack growth.
Conventional finite element approaches for modeling delaminations in laminated composite structures use the Heaviside unit step function at the interfacial nodes in the delaminated zone of the structure to model the possible jumps in the displacement field during “breathing” of the delaminated layers. In quantum mechanics, the Fermi-Dirac distribution applies to Fermion particles whose characteristics are half-integer spins. The present paper uses the Fermi-Dirac distribution function to model a smoother transition in the displacement and the strain fields of the delaminated interfaces during the opening and closing of the delaminated layers under vibratory loads. This paper successfully shows that the Fermi-Dirac distribution function can be used to more accurately model the dynamic effects of delaminations in laminated composite structures. Optimizing the parameters in the Fermi-Dirac distribution function can lead to more accurate modeling of the dynamic and transient behavior of the delaminated zones in laminated composite structures. Further applications of the Fermi-Dirac distribution function in other physics based dynamic models are suggested. This paper also effectively demonstrates how hybrid sensors comprising of out of plane displacement sensors and in plane strain sensors can effectively map a composite structure to detect and locate the delaminated zones. It also shows how simple mode shapes can be used to determine the locations of single and multiple delaminations in laminated composite structures.
A new improved nonlinear transient generalized layerwise theory for modeling embedded discrete and continuous sensor(s) outputs in laminated composite plates with acoustic emission from cracks and embedded delaminations is developed. The computational modeling involves development of a finite element scheme using an improved layerwise laminate theory for a composite laminate plate with embedded discrete and continuous sensors and embedded discrete delaminations. The simulated cases studied included cantilever plates with embedded sensors and embedded delamination under low frequency vibration and square plates with discrete embedded sensors and continuous embedded sensor architecture and embedded discrete delaminations under high frequency acoustic emission. The effect on sensor outputs due to scattering of the acoustic emission due to the presence of delamination is also investigated. It is expected that this analytical model would be a useful tool for numerical simulation of composite laminated structures with embedded delaminations and embedded sensor architecture, particularly since experimental investigation could often be prohibitive to simulate different conditions.
This paper discusses recent advances in modeling and simulation of an artificial neural system and simulation of wave propagation for designing structural health monitoring systems. An artificial neural system was modeled using piezoceramic nerves and electronic components. Wave propagation in a panel is modeled using classical plate theory and a closed-form solution of wave propagation and reflection is obtained. Equations representing a half sine input similar to a projectile impact or a tone burst excitation were added to the existing algorithm that predicts the response of the artificial neural system due to impulse inputs. Firing switches have been modeled in the simulation to predict the sequential firing of the neurons as the waves pass over them. Also, new active fiber sensors have been designed for use in the artificial neural system. Simulated responses of the artificial neural system are shown in this paper and indicate that large neural systems can be formed with hundreds of sensor nodes. Experiments were performed to study a small neural system on a glass fiber panel. Waves were induced in the panel due to a lead break to simulate a crack and due to an impact from an impact hammer. Testing showed the location of a crack could be determined within the unit cell of the neural system for an orthotropic panel.
Health monitoring of aerospace structures can be done passively by listening for acoustic waves generated by cracks, impact damage and delaminations, or actively by propagating diagnostic stress waves and interpreting the parameters that characterize the wave travel. This paper investigates modeling of flexural wave propagation in a plate and the design of sensors to detect damage in plates based on stress wave parameters. To increase understanding of the actual physical process of wave propagation, a simple model is developed to simulate wave propagation in a plate with boundaries. The waves can be simulated by applied forces and moments in the model either to represent passive damage growth or active wave generation using piezoceramic actuators. For active wave generation, the model considers a piezoceramic patch bonded perfectly to a quasi-isotropic glass-epoxy composite plate. Distributed sensors are used on the plate and are modeled as being constructed using active fiber composite and piezoceramic materials. For active wave generation, a moment impulse is generated by the actuation of a piezoceramic patch. The waves generated from the patch are detected by the distributed sensor. For passive sensing of acoustic waves, a step function is used to simulate an acoustic emission from a propagating damage. The resulting acoustic wave is measured by the distributed sensor and produces micro-strains in the sensor nodes. The strains produce a single voltage signal output from the distributed sensor. Computational simulations and animations of acoustic wave propagation in a plate are discussed in the paper. A new method to locate the source of an acoustic emission using the time history of the dominant lower frequency components of the flexural wave mode detected by continuous sensors is also presented.
Recent structural health monitoring techniques have focused on developing global sensor systems that can detect damage on large structures. The approach presented here uses a piezoelectric sensor array system that mimics the biological nervous system architecture to measure acoustic emissions and dynamic strains in structures. The advantage of this approach is that the number of channels of data acquisition used for an N-by-N sensor array can be reduced from N2 to 2N. For large arrays the number of data acquisition channels is tremendously reduced. When transient damage events occur on the structure, the array output time histories can be recorded and the location of the excitation can be accurately determined using combinatorial logic. A trade-off is the difficulty of extracting individual sensor time histories from the array outputs without a neural network or a regressive technique. Only the sums of the sensor strains of each row and column can be exactly calculated using the voltage outputs of the array. The array approach allows efficient use of data acquisition instrumentation for structural health monitoring. Applications for the sensor array include crack and delamination detection, dynamic strain measurement, impact detection, and localization of damage on large complex structures.
KEYWORDS: Sensors, Neurons, Signal processing, Data processing, Composites, Axons, Structural health monitoring, Action potentials, Dendrites, Data communications
This is an overview paper that discusses the concept of an embeddable structural health monitoring system for use in composite and heterogeneous material systems. The sensor system is formed by integrating groups of autonomous unit cells into a structure, much like neurons in biological systems. Each unit cell consists of an embedded processor and a group of distributed sensors that gives the structure the ability to sense damage. In addition, each unit cell periodically updates a central processor on the status of health in its neighborhood. This micro-architectured synthetic nervous system has an advanced sensing capability based on new continuous sensor technology. This technology uses a plurality of serially connected piezoceramic nodes to form a distributed sensor capable of measuring waves generated in structures by damage events, including impact and crack propagation. Simulations show that the neural system can detect faint acoustic waves in large plates. An experiment demonstrates the use of a simple neural system that was able to measure simulated acoustic emissions that were not clearly recognizable by a single conventional piezoceramic sensor.
An embedded active fiber composite tape was investigated for use as a sensor for structural health monitoring. The material has unidirectional piezoceramic fibers with interdigital electrodes on the top and bottom surfaces and is poled in the fiber direction. A long active fiber composite tape with segmented electrodes was modeled as a sensor to measure longitudinal stress waves in a uniform cantilever bar. Only plane longitudinal standing and traveling waves in the bar are modeled. The sensor was connected to an electrical tuning circuit to filter out undesirable noise due to ambient vibrations. The elastic response of the bar was compute in closed form at small time steps, and the coupled piezoceramic constitutive equations and the electrical circuit equations were solved by numerical integration using the Newmark-Beta method. Strain vibration and wave propagation responses were computed in the simulations. The simulations indicate that such a sensor will be capable of detecting damage to the bar from the change sin the wave propagation responses. Further, the sensor has adequate sensitivity to detect fiber breaks in the composite bar. An active fiber composite patch was also tested to measure vibration and simulated acoustic emissions.
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