In this paper, a classification method of flutter test signals based on convolutional neural network (CNN) and Hilbert- Huang transform (HHT) is established, which can be effectively applied to flutter boundary prediction. This method combines convolutional neural network and time-frequency analysis. Firstly, the flutter test signal is preprocessed by Hilbert-Huang transform and labeled according to the actual signal source. The label contains channel information and flutter information. All the signals and labels are composed of the dataset, the dataset is randomly scrambled and 80% of the number is taken as the training set, and the features are extracted, and the classification model is trained by convolutional neural network. The remaining 20% of the dataset is taken as the test set. The test set is used to test the classification model and verify the reliability and accuracy of the model. The accuracy of the final test set is above 90%, which indicates that the model trained by this method can effectively identify the channel information and the flutter information of the signal.
The folding-wing aircraft can obtain appropriate lift and drag by changing the folding angle of the wing. At different stages of the aircraft executing tasks, there can be a corresponding flight state. When the carrier -based aircraft is parked, the occupation space can be reduced by folding by the wing, which can increase the number of carrier -based aircraft. The folding wing drive mechanism is a key technology for folding the wing, which has a key impact on the characteristics of structural transmission. The driving force of the traditional folding wing mechanism is positively related to the size of the drive. Therefore, in the traditional folding wing drive mechanism, if the driving force required when the wing is folded is large, it must use a large size actuators, but this is often limited by the space at the wing shaft. To solve this problem, a folding wing auxiliary drive mechanism is designed in this article. This mechanism uses spring deformation to store the gravity of the wing unfolding process as elastic potential energy and release it when the wing is folded. The use of this mechanism not only increases the driving capacity of the folding wing drive mechanism, but also reduces the power requirement of the main drive. In order to convert the rotation movement when folding the wing into a linear motion, a rotary-to-linear device is designed in this article. In order to eliminate the restrictions on the working load and displacement itinerary in the spring design, this article designed a new type of energy storage spring device: a solid-liquid hybrid spring device, and two spring design schemes are given. Based on the demand of force and displacement strokes, this article gives a detailed design of the auxiliary drive mechanism, and a detailed description of the structural layout and specific operation method is given. On this basis, a key parameter of the auxiliary drive mechanism is given. Finally, the renderings of the auxiliary drive when the wing folding and unfolding are shown.
In order to predict the flutter boundary of the wing under the action of atmospheric turbulence, the obtained turbulence signal is first denoised, and the attenuation signal containing a single mode is obtained by using the variational modal decomposition method. The data containing few attenuation points are extended by using machine learning methods, and the matrix pencil method is used to identify the modes. Finally, the stability criterion is calculated by using the modal parameters, The flutter boundary is calculated from these data. In this paper, the wind tunnel test data are used to analyze and solve the modal, and the flutter boundary is accurately predicted in advance.
This paper is based on the continuous wavelet transform to achieve modal parameter identification of the response signal of a wing under environmental excitation. Suitable wavelet basis functions are selected for the characteristics of flutter data to achieve a good time-frequency analysis of flutter data. And the endpoint effect is effectively suppressed by the predictive extension method of support vector machine. The identification of wavelet ridges and the wavelet cross-section are obtained through a crazy creep algorithm to achieve the identification of modal damping in the chattering data. Finally, the method is applied to the wind tunnel test data of a 3d-printed wing to identify its modal parameters, and is compared with its numerical simulation data to verify the reliability of CWT applied to the identification of modal parameters of wing flutter data under environmental excitation.
A flutter boundary prediction method based on HHT, and machine learning is proposed to predict the flutter velocity before the wind speed reaches the subcritical state. Natural excitation technique is used to extract impulse response signals. EMD (empirical Mode decomposition method) is used to decompose the signal. Hilbert spectrum was obtained and analyzed by HHT to decompose the signal. The analysis methods included HHT spectrum and marginal spectrum analysis, so as to extract the characteristic quantity and establish the classification model according to different flight states. Then, regression models were established under different flutter modes for flutter degree analysis. During the prediction, according to the classification performance of the data to be measured, the flutter degree analysis result is weighted to obtain the flutter degree corresponding to the current wind speed, and then the flutter wind speed is calculated. In the selection of machine learning algorithm, naive Bayes algorithm, K-nearest neighbor algorithm and other machine learning algorithms are used to construct the classification model, linear regression, Gaussian process regression and so on are used to construct the regression model. The results show that the K-nearest neighbor algorithm performs best in the classification algorithm, while the Gaussian process regression algorithm performs best in the regression algorithm. Through the cross-validation of the test data, the proposed method can accurately predict the critical flutter velocity when it is far away from the flutter boundary through flutter mode recognition and flutter degree analysis.
In this paper, a squid-like jet propeller actuated by piezoelectric pumps is designed, which can realize underwater pulsedjet process. The squid-like jet propeller comprises a bionic mantle and a rigid framework. The bionic mantle is a sealedflexible cavity, and a plurality of piezoelectric pumps are embedded on the bionic mantle to continuously absorbwaterinto the cavity. When the piezoelectric pump works, water is sucked into the flexible cavity, when the pressure inthecavity reaches a certain value, the jet is ejected through the nozzle. Firstly, this paper studies the structural designof thebionic mantle, then studies the influence of the number and the water absorption performance of piezoelectric pumpsaswell as water absorption time on the propulsion performance of the squid-like jet propeller, and then studies thestructural deformation of the bionic mantle and the variation of the parameters of the jet propeller during the pulsedjet process. The squid-like jet propeller can control the pulsed jet cycle and other propulsion performance.
KEYWORDS: Signal processing, Wind measurement, Signal attenuation, Detection theory, Turbulence, Signal to noise ratio, Error analysis, Electronic filtering, Data processing, Data modeling
A new method of mode parameter identification based on Extreme-point Symmetric Mode Decomposition (ESMD) and Matrix Pencil Method (MPM) is proposed for processing wind tunnel test data.The proposed method first decomposes the test data to a series of narrow-band signals by band-pass filtering.Then, the ESMD method is used to perform modal decomposition to obtain several single-mode response signals.Next, each singlemode response signal is processed using Natural Excitation Technique(NExT) to obtain a free attenuation response signals.Finally, the mode parameters were identified by the MPM.After the verification of simulation data, the proposed method is applied to identifying the mode parameters of the wind tunnel test data, and the results are compared with the mode parameter identification results based on the empirical mode decomposition (Empirical Mode Decomposition, EMD). The results show that the proposed method can better identify the mode parameters of the structure from the wind tunnel test data with good applicability and sufficient identification accuracy.
In order to predict the flutter boundary of the wing under turbulent excitation, wavelet decomposition is used to preprocess the signal, and the free attenuation signal is extracted based on CEEMDAN and natural excitation technology. The matrix pencil method is used to identify the modal parameters. Finally, the Z-W method is used to determine the aircraft loss stability, fit the change curve of the judgment and extrapolate the flutter boundary. The modal parameters of the simulated turbulence excitation signal are identified, the numerical simulation of the flat wing model is carried out, and the wind tunnel flutter test data of a single wing model are calculated. The results show that: using matrix pencil method to process the free attenuation signal obtained by ceemdan, wavelet de-noising and natural excitation technology, the modal parameters of turbulent excitation response can be identified more accurately, combined with Z-W method, the flutter boundary can be predicted in the case of early wind speed, and the test safety can be improved.
Variant aircraft is an aircraft that can change its shape during flight to obtain different aerodynamic characteristicstocope with different flight environments and complete different flight tasks. The wing with variable camber at the trailingedge, as a relatively easy deformation method in structure, has attracted much attention in the research field. In order torealize smooth and flexible deformation of wing with variable camber at trailing edge, distributed driving mode must beadopted. In this paper, a multi-section distributed driving device based on fiber reinforced flexible cavity with internal pressure is proposed. The influence of fiber braided sleeve on the flexible cavity with internal pressure, the internal structure layout design of the driving device, the connection between components in the device and the connectionbetween the device and the wing are studied. The driving device has been applied to a wing with variable trailingedgecamber, and satisfactory results have been obtained. It is proved that the driving device can improve the drivingreliability on the basis of meeting the driving requirements, so that the whole trailing edge variable camber wingcanmaintain good continuity during deformation.
Based on the time series model, the modal parameters of the continuous variable flutter test response signal are identified, and a set of flutter boundary prediction methods suitable for turbulent excitation is developed in this paper. In order to ensure the accuracy of the method, a modal parameter identification method is used to analyze the traditional autoregressive model (AR) and the time-varying autoregressive model (TVAR), and compare the accuracy of the two models. Finally, the method is applied to the flutter boundary prediction of turbulence signals. The prediction method combines the time series model with the stability criterion, constructs the stability parameters of the response signal, and the prediction results of flutter critical velocity are obtained by fitting and extrapolation. The numerical example shows the analysis results of the two models and proves the feasibility and effectiveness of the method. Finally, this method is used to predict the flutter boundary of low-speed wind tunnel test data, and the prediction error is less than 5%.
Morphing aircraft can sense load and attitude in real time and adaptively deformed according to different flight environments and tasks. They can achieve excellent performance in different environments and tasks. It is one of the main hotspots in recent years. However, the torsional stiffness of deformable wing structure with flexible skin will be greatly reduced, so the wing is prone to torsion during flight, which is not conducive to flight. In this paper, a stiffness compensate device is proposed. When the wing is subjected to torque, the rotating torque is transmitted to the stiffness compensation device, which is transformed and transmitted inside the device, and finally balanced by the spring inside the device, so as to compensate for the reduced torsional stiffness of the wing due to the use of flexible skin and increase the torsional resistance of the wing.The mechanical properties of the device are studied by theoretical analysis and a case is analyzed. The ability of the device to improve the torsional stiffness of the wing and its influencing factors are analyzed in this paper. The feasibility of the device is verified. The torsion resistance of the deformable wing can be greatly enhanced by this device.
KEYWORDS: Wavelets, Denoising, Signal processing, Interference (communication), Signal to noise ratio, Signal analyzers, Wavelet transforms, Mechanics, Astronomical engineering, Analytical research
The most important step of flutter analysis is to predict the flutter boundary of the aircraft to ensure that there will be no flutter in the flight envelope. However, due to the low signal-to-noise ratio and modal density of flutter signal, traditional modal identification methods cannot effectively extract the modal information of the data. Therefore, in order to solve this problem, this paper proposes a method which combines wavelet denoising and a masking signal. Wavelet denoising can effectively reduce the noise interference, and masking signal can effectively alleviate the problem of mode mixing which improves the accuracy of the signal modal identification.
The variable camber wing can significantly improve the aerodynamic characteristics of the aircraft and is an important form of morphing aircraft. Flexible skin technology is one of the key technologies. According to the skin deformation features of the variable camber wing, a flexible skin form is proposed in this paper. The fishbone-shaped reinforcing structure (FBRS) is applied as the main component of the flexible skin to bear aerodynamic loads. Rubber material with excellent deformation ability wraps the FBRS to obtain a smooth and flat skin surface. Thorn-shaped branches on adjacent FBRSs are arranged in a staggered manner. In order to increase the out-of-plane stiffness of the flexible skin, the flexible skin needs to be used in combination with the corrugated structure. Each wave crest of the corrugated structure is connected with the FBRS of the flexible skin. By setting the wave crest of the corrugated structure into a platform shape, a stable connection between the FBRS and the corrugated structure is maintained. In this paper, the stiffness expressions of FBRS and corrugated structure are derived. The chordwise deformation capacity and out-of-plane bearing capacity of the flexible skin are verified by the method of finite element simulation. The results show that the FBRS can transmit aerodynamic loads well and maintain the smoothness and flatness of the rubber surface. Supported by a corrugated structure, this type of flexible skin has good chordwise deformation ability and high out-of-plane bearing capacity.
KEYWORDS: Signal processing, Signal attenuation, Bandpass filters, Signal to noise ratio, Data processing, Mathematical modeling, Turbulence, Electronic filtering, Mechanics, Radon
While identifying the parameters of IMFs from Empirical Mode Decomposition, by Hilbert-Huang Transform, a piece of approximately linear data segment is necessary for a specific result. The select of the data segment will directly influence accuracy of the parameters. The time for getting the approximately linear data segment is required to be as short as possible. The paper uses Least Square Series-piecewise Linear Fitting method to divide data into pieces, then chooses several pieces with the highest goodness-of-fit, and takes each median as basis to change the length, for higher goodness-of-fit. The needed data segment is achieved in the case that this data segment can still reflect the inherent parameters. This paper brings some examples to verify that the approach is feasible and exact.
Morphing aircraft can change external shape in flight according to different flight environments and tasks, and improve flight performance maximumly. Among them, the morphing wing can improve the aerodynamic performance efficiently and has become one of the hot spots in recent years. One of the key technologies for morphing wing is flexible skin technique. Aiming at the conflict between in-plane deformation and out-of-plane bearing capacity of flexible skin structure design, a zero Poisson's ratio hybrid honeycomb structure was designed. The strips are added to the honeycomb structure to form a hybrid honeycomb, which increases the out-of-plane bending stiffness. Three different shapes of honeycomb grid elements were proposed, which are cruciform, square, and H-shaped. By adjusting the shape and size parameters of the three kinds of honeycomb grid elements and the height and quantity of the laying strips, the in-plane deformation mechanism of each element was analyzed by the representative volume element method, as well as the variation of mechanical properties with the element and strip shape parameters. The mechanical properties of the hybrid honeycomb structure were analyzed by finite element simulation. Considering the requirements of the variable camber trailing edge wing, a flexible skin which has capacity of out-of-plane bending resistance was constituted by covering elastic panel over the surface of zero Poisson's ratio hybrid honeycomb. The flexible skin structure has good airtightness and smooth surface. Also, it meets the requirements of in-plane unidirectional deformation along with out-of-plane bearing capacity.
This article proposes a new method of predicting flutter boundary online. In flutter test which is under continuous wind excitation, the primary objective is to get vibration response of critical position and determine the critical mode for flutter prediction. The frequency spectrum graphs are drawn to get the peaks round the modal frequency. As the windtunnel test goes on, more signal data are obtained to get the relation curve between peak-reciprocal and airstream speed. Then the signal points are drawn to get the fitting curve and the flutter boundary is predicted by extrapolation. The flutter point predicted is updating until the threshold between real-time airstream speed and extrapolated speed meets the requirements.
For the purpose of predicting the flutter boundary in real time during flutter flight tests, two time series models accompanied with corresponding stability criterion are adopted in this paper. The first method simplifies a long nonstationary response signal as many contiguous intervals and each is considered to be stationary. The traditional AR model is then established to represent each interval of signal sequence. While the second employs a time-varying AR model to characterize actual measured signals in flutter test with progression variable speed (FTPVS). To predict the flutter boundary, stability parameters are formulated by the identified AR coefficients combined with Jury’s stability criterion. The behavior of the parameters is examined using both simulated and wind-tunnel experiment data. The results demonstrate that both methods show significant effectiveness in predicting the flutter boundary at lower speed level. A comparison between the two methods is also given in this paper.
In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.
As the covering of morphing wings, flexible skin is required to provide adequate cooperation deformation, keep the smoothness of the aerodynamic configuration and bear the air load. The non-deformation direction of flexible skin is required to be restrained to keep the smoothness during morphing. This paper studies the deformation mechanisms of a cruciform honeycomb under zero Poisson’s ratio constraint. The morphing capacity and in-plane modulus of the cruciform honeycomb are improved by optimizing the shape parameters of honeycomb unit. To improve the out-of-plane bending capacity, a zero Poisson’s ratio mixed cruciform honeycomb is proposed by adding ribs into cruciform honeycomb, which can be used as filling material of flexible skin. The mechanical properties of the mixed honeycomb are studied by theoretical analysis and simulation. The local deformation of flexible skin under air load is also analyzed. Targeting the situation of non-uniform air load, a gradient density design scheme is referred. According to the design requirements of the variable camber trailing edge wing flexible skin, the specific design parameters and performance parameters of the skin based on the mixed honeycomb are given. The results show that the zero Poisson’s ratio mixed cruciform honeycomb has a large bending rigidity itself and can have a better deformation capacity in-plane and a larger bending rigidity out-of-plane by optimizing the shape parameters. Besides, the designed skin also has advantages in driving force, deformation capacity and quality compared with conventional skin.
A finite element model of scarf repaired laminates in tension is built in this paper. The ultimate strengths of the repaired
structures are calculated with progressive damage analysis method. The predicted results agreeing well with the
experimental results which means the model is effective. The failure model of these structures is adhesive failure which
is obtained by analyzing the failure process of these structures. The effects of scarf ratio and scarf depth on the ultimate
strength are analyzed which indicates that the ultimate strength can be improved by both decreasing scarf ratio and
decreasing scarf depth .The results of this study can provide some references for the design of scarf repaired composite
laminates.
This paper presents a novel method to monitor crack length which based on binary tree support vector machines (BTSVM). In this method, matching pursuit method with Chirplet atom is applied to extract the matching parameters as feature vectors to train and test in the BT-SVM algorithm. Then one simulation of lug joint is carried out for studying the effect of crack extension on Lamb wave signals propagation. Fatigue loading experiments on lug joints are carried out at last. The results show that this new method can monitor the length of fatigue crack effectively.
In order to identify multiple damage in the structure, a method of multiple damage identification and imaging based on
the effective Lamb wave response automatic extraction algorithm is proposed. In this method, the detected key area in
the structure is divided into a number of subregions, and then, the effective response signals including the structural
damage information are automatically extracted from the entire Lamb wave responses which are received by the
piezoelectric sensors. Further, the damage index values of every subregion based on the correlation coefficient are
calculated using the effective response signals. Finally, the damage identification and imaging are performed using the
reconstruction algorithm for probabilistic inspection of damage (RAPID) technique. The experimental research was
conducted using an aluminum plate. The experimental results show that the method proposed in this research can quickly
and effectively identify the single damage or multiple damage and image the damages clearly in detected area.
This paper presents a novel two-layer spectral finite element model, consisting of PZT wafer and host structure, to
simulate PZT-induced Lamb wave propagation in beam-like and plate-like structures. Based on the idea of equal
displacement on the interface between PZT wafer and host structure, the one-dimensional spectral beam element of PZT-host
beam and two-dimensional spectral plate element of PZT-host plate are considered as one hybrid element,
respectively. A novel approach is proposed by taking the coupling effect of piezoelectric transducers in the thickness
direction into account. The dynamic equation of the two-layer spectral element is derived from Hamilton’s principle.
Validity of the developed spectral finite element is verified through numerical simulation. The result indicates that,
compared with the conventional finite element method (FEM) based on elasticity, the proposed spectral finite element is
proved to have a high accuracy in modeling Lamb wave propagation, meanwhile, significantly improve the calculation
efficiency.
This work focuses on an unsupervised, data driven statistical approach to detect and monitor fatigue crack growth in lug
joint samples using surface mounted piezoelectric sensors. Early and faithful detection of fatigue cracks in a lug joint can
guide in taking preventive measures, thus avoiding any possible fatal structural failure. The on-line damage state at any
given fatigue cycle is estimated using a damage index approach as the dynamical properties of a structure change with
the initiation of a new crack or the growth of an existing crack. Using the measurements performed on an intact lug joint
as baseline, damage indices are evaluated from the frequency response of the lug joint with an unknown damage state.
As the damage indices are evaluated, a Bayesian analysis is committed and a statistical metric is evaluated to identify
damage state(say crack length).
A damage localization method for plate-like structure is developed based on Lamb waves and matching pursuits method
with chirplet dictionary. The matching pursuits method is employed to decompose Lamb wave signals into a linear
expansion of several chirplet atoms with a fast realization algorithm. The relationship between Lamb wave's dispersion
and chirplet's chirp rate is established, which can be used to identify the modes of Lamb waves. Then a method for
damage localization is developed based on the difference between the baseline signals and the damaged signals. The
effectiveness and accuracy of the proposed method for identifying the modes and locating defects are demonstrated by
the simulation and experimental results of isotropic plate structure and honeycomb sandwich composite structure.
Recently, an adaptive extended Kalman filter (AEKF) approach has been proposed for the damage identification and
tracking of structures. Simulation and experimental studies have demonstrated that this AEKF approach is capable of
tracking the damages for linear structures. In this paper, an experimental study is conducted and presented to verify the
capability of the adaptive extended Kalman filter (AEKF) approach for identifying and tracking the damages in
nonlinear structures. A base-isolated building model, consisting of a scaled building model mounted on a rubber-bearing
isolation system, has been tested experimentally in the laboratory. The non-linear behavior of the base isolators is
modeled by the Bouc-Wen model. To simulate the structural damages during the test, an innovative device, referred to as
the stiffness element device (SED), is proposed to reduce the stiffness of either the upper story of the structure or the
base isolator. Two earthquake excitations have been used to drive the test model, including the El Centro and Kobe
earthquakes. Various damage scenarios have been simulated and tested. Measured acceleration response data and the
AEKF approach are used to track the variation of the stiffness during the test. The tracking results for the stiffness
variations correlate well with that of the referenced values. It is concluded that the AEKF approach is capable of tracking
the variation of structural parameters leading to the detection of structural damages.
An objective of the structural health monitoring system is to identify the state of the structure and to detect the
damage when it occurs. Analysis techniques for the damage identification of structures, based on vibration data
measured from sensors, have received considerable attention. Recently, a new damage tracking technique, referred to as
the adaptive quadratic sum-square error (AQSSE) technique, has been proposed, and simulation studies demonstrated
that the AQSSE technique is quite effective in identifying structural damages. In this paper, the adaptive quadratic sumsquare
error (AQSSE) along with the reduced-order finite-element method is proposed to identify the damages of
complex structures. Experimental tests were conducted to verify the capability of the proposed damage detection
approach. A series of experimental tests were performed using a scaled cantilever beam subject to the white noise and
sinusoidal excitations. The capability of the proposed reduced-order finite-element based adaptive quadratic sum-square
error (AQSSE) method in detecting the structural damage is demonstrated by the experimental results.
Propagation of torsional elastic waves in the clad core is addressed in this paper. The shear velocity of the core is slightly
smaller than that in the cladding. Core with cladding of different finite thickness and infinite thickness is investigated.
Two types of modes, guided and leaky modes are examined with the discussion of motion in waveguide. Phase, group,
and energy velocities, cutoff frequencies are analyzed and the results of first three modes are presented. The change of
dispersion curves due to variation of thickness of cladding is discussed and it is found that when the thickness increases
the results of finite clad core will approach those of infinite clad core in guided mode, but not in leaky mode. Below
cutoff frequencies the wavenumber becomes complex in infinite clad core, while it is pure imaginary in finite clad cores.
The group and energy velocity are presented and in leaky mode the group velocity becomes abnormal, while the energy
velocity is physically meaningful.
An early detection of structural damages is critical for the decision making of repair and replacement maintenance in
order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration
data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The
traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman
filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some
SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external
excitations (inputs) are not measured, referred to as the LSE with unknown inputs (LSE-UI) and the EKF with unknown
inputs (EKF-UI). Also, new analysis methods, referred to as the sequential non-linear least-square estimation with
unknown inputs and unknown outputs (SNLSE-UI-UO) and the quadratic sum-square error with unknown inputs
(QSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not
measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be
compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on
experimental data obtained through a series of experimental tests using a small-scale 3-story building model with white
noise excitation. The capability of the LSE-UI, EKF-UI, SNLSE-UI-UO and QSSE-UI approaches in tracking the
structural damages will be demonstrated.
Damage identification of structures is an important task of a health monitoring system. The ability to detect damages
on-line or almost on-line will ensure the reliability and safety of structures. Analysis methodologies for structural
damage identification based on measured vibration data have received considerable attention, including the least-square
estimation (LSE), extended Kalman filter (EKF), etc. Recently, new analysis methods, referred to as the sequential non-linear
least-square estimation (SNLSE) and quadratic sum-squares error (QSSE), have been proposed for the damage
tracking of structures. In this paper, these newly proposed analysis methods will be compared with the LSE and EKF
approaches, in terms of accuracy, convergence and efficiency, for damage identification of structures based on
experimental data. A series of experimental tests using a small-scale 3-story building model have been conducted. In
these experimental tests, white noise excitations were applied to the model, and different damage scenarios were
simulated and tested. Here, the capability of the adaptive LSE, EKF, SNLSE and QSSE approaches in tracking the
structural damage are demonstrated using experimental data. The tracking results for the stiffness of all stories, based on
each approach, are compared with the stiffness predicted by the finite-element method. The advantages and drawbacks
for each damage tracking approach will be evaluated in terms of the accuracy, efficiency and practicality.
KEYWORDS: Surface conduction electron emitter displays, Sensors, Error analysis, Solids, Finite element methods, Chemical elements, Data analysis, Structural health monitoring, Motion models, Aerospace engineering
The detection of structural damage is an important objective of structural health monitoring systems. Analysis
techniques for the damage detection of structures, based on vibration data measured from sensors, have been studied
without experimental verifications. In this paper, a newly proposed data analysis method for structural damage
identifications, referred to as the adaptive quadratic sum squares error (AQSSE), will be verified experimentally. A
series of experimental tests using a scaled 3-story building model have been conducted recently. In the experimental
tests, white noise excitations were applied to the top floor of the model, and different damage scenarios were simulated
and tested. These experimental data will be used to verify the capability of the AQSSE approach in tracking the
structural damage. The tracking results for the stiffness of all stories, based on the AQSSE approach, are compared with
the stiffness predicted by the finite-element method. Experimental results demonstrate that the AQSSE approach is
capable of tracking the structural damage with reasonable accuracy.
This paper presents a semi-active flutter control strategy for a high-aspect-ratio (HAR) wing using multiple
magnetorheological (MR) dampers. In this paper, the aeroelastic behavior of the system is first investigated by
establishing the aeroelastic equations of a HAR wing-aileron system. The strip theory is employed for calculating the
unsteady aerodynamic loads. Then the semi-active aeroelastic control system with multiple MR dampers is modeled. The
clipped-optimal control algorithm is performed for controlling the MR dampers to suppressing the flutter of the
aeroelastic system. A passive flutter control of the system is also performed for the purpose of comparison. Numerical
simulation results show that the semi-active control strategy based on multiple MR dampers holds promise in
suppressing the flutter of the HAR wing-aileron system.
It is well known that vibration-based damage detection methods lack sensitivity in modal frequencies to
small changes in mass, stiffness, and damping parameters induced by damage. To circumvent this deficiency, in this paper a scheme through feedback control together with coherence method is first
employed to enhance sensitivity the occurrence and location of damage through few of the lower natural frequencies. This sensitivity enhancement is based on pole placement from a single-point feedback control to compute the control gains. Relying on a priori knowledge of how certain damage scenarios affect modal
properties, a coherence method correlates measured and predicted modal frequency shifts for a given set of damage scenarios to locate the damage. Numerical results show that the method with closed-loop control increases both the accuracy of locating damage and the ability to tolerate environmental noise. Subsequent to the fine sensitivity to locating the damage position, restoration to original dynamic structural performance in terms of few of the lower natural frequencies of the damaged structure is conducted by the feedback control using distributed actuation surrounding the damage area (multi-point control). Simulation results show that the dynamic characteristics of damaged structures can be successfully restored by applying distributed actuation that can be induced from the voltage by the distributed piezoelectric actuators.
In this paper a pre-stack reverse-time migration concept of signal processing techniques is developed and adapted to guided-wave propagation in composite structure for multi-damage imaging by experimental studies. An anisotropic laminated composite plate with a surface-mounted linear piezoelectric ceramic (PZT) disk array is studied as an example. At first, Mindlin Plate Theory is used to model Lamb waves propagating in laminates. The group velocities of flexural waves are also derived from dispersion relations and validated by experiments. Then reconstruct the response wave fields with reflected data collected by the linear PZT array. Reverse-time migration technique is then performed to back-propagate the reflected energy to the damages using a two-dimensional explicit finite difference algorithm and damages are imaged. Stacking these images together gets the final image of multiple damages. The experimental results show that the pre-stack migration method is hopeful for damage detection in composite structures.
In this paper, a wavelet-based built-in damage detection and identification algorithm for carbon fiber reinforced polymer (CFRP) laminates is proposed. Lamb waves propagating in laminates are first modeled analytically using higher-order plate theory and compared them with experimental results in terms of group velocity. Distributed piezoelectric transducers are used to generate and monitor the fundamental ultrasonic Lamb waves in the laminates with narrowband frequencies. A signal processing scheme based on wavelet analysis is applied on the sensor signals to extract the group velocity of the wave propagating in the laminates. Combined with the theoretically computed wave velocity, a genetic algorithms (GA) optimization technique is employed to identify the location and size of the damage. The applicability of this proposed method to detect and size the damage is demonstrated by experimental studies on a composite plate with simulated delamination damages.
An important objective of health monitoring systems for civil infrastructures is to identify the state of the structure and to detect the damage when it occurs. System identification and damage detection based on measured vibration data have received considerable attention recently. Frequently, the damage of a structure may be reflected by a change of some system parameters, such as a degradation of the stiffness. In this paper, we propose an adaptive tracking technique, based on the extended Kalman filter approach, to identify the structural parameters and their changes. The proposed technique is capable of tracking the abrupt change of system parameters from which the event and severity of structural damages can be detected. Our adaptive filtering technique is based on the current measured data to determine the parametric variation so that the residual error of the estimated parameters is contributed only by noises. The proposed technique is applicable to linear and nonlinear structures. Simulation results for tracking the parametric changes of linear and nonlinear hysteretic structures are presented to demonstrate the application and effectiveness of the proposed technique in detecting the structural damages using vibration data from the health monitoring system.
KEYWORDS: Bridges, Sensors, Databases, Statistical analysis, Structural health monitoring, Rule based systems, Internet, Data archive systems, Computing systems, Data processing
Smart Health Monitoring System (SHMS) is a set of integrated system of hardware and software designed to automatically collect and analyze the data from a faraway bridge. The real-time data can be preprocessed in the sub-workstation on the bridge then transferred to the main server with a wired or wireless internet access. SHMS is based on the statistical analysis of the static and dynamic characteristics of structures. In order to automate the procedure of processing and analyzing all the raw data, a rule-based expert system was developed for the monitoring system with Bootstrap Method. In general, the estimation of parameters from measurement always contains systematic perturbations and random fluctuations. The systematic perturbations mainly come from periodic environmental factors, especially temperature. Random fluctuations result from irregular disturbance including instrumentation sources and numerical processing algorithms. The former can be identified and characterized. Based on the historical data, a set of correction models have been built to remove the influence from systematic perturbations. Random fluctuations are difficult to be characterized by traditional statistical methods. But with Bootstrap Method, we can minimize the random error.
Fuzzy logic control (FLC) and genetic algorithms (GA) are integrated into a new approach for the semi-active control of structures installed with MR dampers against severe dynamic loadings such as earthquakes. The interactive relationship between the structural response and the input voltage of MR dampers is established by using a fuzzy controller rather than the traditional way by introducing an ideal active control force. GA is employed as an adaptive method for optimization of parameters and for selection of fuzzy rules of the fuzzy control system, respectively. The maximum structural displacement is selected and used as the objective function to be minimized. The objective function is then converted to a fitness function to form the basis of genetic operations, i.e. selection, crossover, and mutation. The proposed integrated architecture is expected to generate an effective and reliable fuzzy control system by GA’s powerful searching and self-learning adaptive capability.
The development of implementable control strategies that can fully utilize the capabilities of semi-active control devices is a challenging task due to the intrinsically nonlinear characteristics of the problem. In this study, a multivariable adaptive fuzzy controller is derived for a multiple-input and multiple-output nonlinear system, and a multivariable adaptive fuzzy control strategy is proposed accordingly for the use of magnetorheological (MR) dampers to protect buildings against dynamic hazards, such as severe earthquakes and strong winds. The proposed control strategy involves the design of fuzzy controllers and adaptation laws. The control objective is set to minimize the difference between some desirable response and the response of the combined system by adaptively adjusting MR dampers. The use of the adaptation law eliminates the needs for acquiring characteristics of the combined system in advance. The combination of the fuzzy controller and the adaptation law provides a robust control mechanism that can be used to protect nonlinear or uncertain structures subjected to random loads. Numerical and analytical results are presented to illustrate the application of the proposed control strategy.
Because of the intrinsically nonlinear nature of semiactive control devices, development of control strategies that are practically implementable and can fully utilize the capabilities of these promising devices is an important and challenging task. In this study, we propose the use of an adaptive fuzzy strategy for the control of a structure installed with a magnetorheological (MR) damper. The proposed adaptive fuzzy control strategy involves the design of a fuzzy controller and an adaptation law for the combined structure-MR damper system. The objective control is to minimize the difference between a desirable response and the response of the combined system by intelligently adjusting its active component, the MR damper. The use of the adaptation law requires on-line monitoring of system response but eliminates the needs of acquiring any characteristics of the combined system in advance. The combination of the fuzzy controller and the adaptation law provides a robust control strategy that can be used on a nonlinear or uncertain system under random loads. A numerical example which involves controlling a single- degree-of-freedom structure under earthquake excitation using a MR damper is studied and presented. The simulated results indicate that the proposed adaptive fuzzy control strategy is quite effective and appropriate for the use of semiactive devices such as the MR damper.
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