Viscoelastic Sandwich Structures Research Articles

Unsupervised graph anomaly detection with discriminative embedding similarity for viscoelastic sandwich cylindrical structures

In the detection of slipping anomalies in viscoelastic sandwich cylindrical structures (VSCS), conventional methods may encounter challenges due to the extremely rare and weak nature of slipping signals. This study focuses on normal signals and introduces unsupervised graph representation learning (UGRL) with discriminative embedding similarity for VSCS's detection. UGRL involves data preprocessing, model embedding, and matrix reconstructing. Association graphs are constructed based on sample similarities for yielding adjacency and attribute matrices. Subsequently, the matrices undergo embedding and reconstruction via various network modules to enhance graph data characterization. Detection indicators are derived by calculating embedding similarities and reconstruction errors, and thresholds are constructed using these indicators to enable efficient anomaly detection. The experiments in VSCS slipping dataset effectively indicate the superiority of the proposed method.

A Novel Method to the Aging Feature Extraction of Viscoelastic Sandwich Structures

The viscoelastic sandwich structure exhibits highly nonlinear dynamic characteristics, and minimal changes in signal caused by aging state alterations, thus posing a challenge for extracting its aging characteristics. A method is proposed for extracting aging features from viscoelastic sandwich structures using variational mode decomposition (VMD) and permutation entropy (PE). First, the original dynamic response signal is decomposed by VMD to acquire several intrinsic mode functions (IMF). Then the permutation entropy characteristics of each IMF are calculated. The research results show that there are obvious differences in the distribution of permutation entropy corresponding to each IMF component, which indicates that the permutation entropy feature extracted based on the variational modal decomposition is more susceptive to the change of the aging state of the structure, thus verifying the availability of this method for feature extraction.

Transverse Vibration of Viscoelastic Sandwich Structures: Finite Element Modeling and Experimental Study.

In the present work, the nonlinear vibration behavior of elastic-viscoelastic-elastic sandwich (EVES) beams is studied. A finite element (FE) equation taking intoaccount the transverse compression deformation of the viscoelastic core for the EVES beams is derived. In order toaccurately characterize the frequency-dependent feature of the viscoelastic materials layer, athird-order seven-parameter Biot model isused. A 2-node 8-DOF element is established to discretize the EVES beams. The experimental testing onEVES beams validates the numerical predication of the FE model. Numerical and analytical investigations are carried on a series of EVES beams with different thicknesses. The results indicate that the presented FE model has better accuracy in predicting the natural frequency of the sandwich beams, and in predicting damping, the accuracy is related to the thickness of each layer. The results of this paper have important reference values for the design and optimization of the viscoelastic sandwich structure.

Aging state detection of viscoelastic sandwich structure using redundant second generation wavelet packet transform and fuzzy support vector data description

Viscoelastic sandwich structure plays an important role in mechanical equipment, nevertheless viscoelastic material inevitably suffers from gradual aging. For guaranteeing the operation safety of mechanical equipment, it is urgent to perform the aging state detection of viscoelastic sandwich structure with vibration response signal analysis. However, the structural vibration response signal is non-stationary and its variation caused by the structural aging state change is very puny, and the abnormal state samples is lacking. The vibration-based structural aging state detection has become a challenging task. Therefore, a novel method based on redundant second generation wavelet packet transform (RSGWPT) and fuzzy support vector data description (FSVDD) is proposed for this task. For extracting sensitive aging feature information, RSGWPT is introduced to process the structural vibration response signal, and multiple energy features are extracted from the frequency-band signals to reflect structural aging state change. For accurate and automatic aging state identification, by fusing fuzzy theory, FSVDD only uses the normal state samples for training and can identify the abnormal severity degrees is developed to identify the structural aging states. The proposed method is applied on a viscoelastic sandwich structure to validate its effectiveness, and different structural aging states are created through the accelerated aging of viscoelastic material. The analysis results show the outstanding performance of the proposed method.

A Multi-Sensor Data Fusion Method for Intelligent Aging Condition Identification of Viscoelastic Sandwich Structure

Viscoelastic sandwich structure is playing an important role in mechanical equipment. The aging condition identification of viscoelastic sandwich structure is essential for monitoring structural health condition and guaranteeing the safe operation of mechanical equipment. The development of a reliable intelligent aging condition identification method is an ongoing attempt. By analyzing vibration response signals, a multi-sensor data fusion method using multi-scale permutation entropy (MPE) and ensemble-based incremental support vector machine (EISVM) is proposed in this paper. In this method, for obtaining complementary information, multiple sensors are mounted on the different locations of viscoelastic sandwich structure. For extracting effective feature information, MPE is introduced to reflect the structural aging condition change, and multiple feature sets of multiple sensors are obtained. With distance evaluation technique, multiple sensitive feature sets are respectively selected from the multiple original feature sets to discard irrelevant or redundant features. Based on ensemble learning and incremental learning, EISVM is developed to fuse all sensitive feature sets from different sensors for reliable intelligent classification. The proposed method is verified with a well-designed viscoelastic sandwich structure in which the viscoelastic layer subjected to accelerated aging. The testing results show the outstanding performance of the proposed method.

Dynamic response of viscoelastic multiple-core sandwich structures

This work focuses on the free and forced vibrations of sandwich beams and plates hosting an arbitrary number of damping cores. A fractional derivatives Zener-type model is adopted for representing the frequency-dependent viscoelastic behaviour, along with conventional series developments. The structural models are formulated within an established variable kinematics approach, which enables to investigate the role of specific assumptions and to identify the most accurate model at a least number of degrees of freedom. Approximate solutions are found by a computationally efficient Ritz method that allows to take into account any type of boundary conditions. Modal loss factors and damped eigenfrequencies are obtained from a complex eigenvalue problem, for which a modal strain energy approach or a complex eigensolution can be employed. Frequency responses can be computed by a direct approach or by elementary modal projection algorithms. Results are reported for conventional and innovative sandwich configurations. Different modelling and solution strategies are compared and the role of transverse normal deformation of the mechanically weak viscoelastic plies is particularly emphasised.

Study of damped vibrations of a vibroacoustic interior problem with viscoelastic sandwich structure using a High Order Newton solver

The aim of this study is to compute damped eigenfrequencies and modes of a vibroacoustic interior problem with fluid-structure coupling. Damping is introduced using a sandwich structure with viscoelastic core. In this paper, the coupled problem is solved by a High Order Newton (HON) solver, based on homotopy and perturbation techniques. Thus, the initial non-linear problem is turned into a set of linear algebraic systems, easier to solve. Comparison of the results obtained by the HON solver with those obtained by the Newton classical method highlights the efficiency of the proposed method. Finally, the mechanical behavior of the coupled damped problem is studied.

Looseness condition feature extraction of viscoelastic sandwich structure using dual-tree complex wavelet packet-based deep autoencoder network

It is significant to perform looseness condition detection of viscoelastic sandwich structures to avoid serious accidents. Due to the multilayer characteristic of the viscoelastic sandwich structure, the vibration response signal of such structures is nonlinear and nonstationary. Furthermore, the looseness condition feature signal contained in the vibration response signal is very puny. Condition feature extraction has become a challenging task in the looseness condition detection of viscoelastic sandwich structures. Therefore, a novel method called dual-tree complex wavelet packet-based deep autoencoder network is proposed for this task. First, the vibration response signal of the viscoelastic sandwich structure is decomposed by dual-tree complex wavelet packet transform and the sub-band signals which contain rich energy are extracted. Then, the energies of the extracted sub-band signals are calculated to form a feature set. Finally, a deep autoencoder network is established to fuse the feature set, and the fused feature is viewed as the detection index to detect the looseness condition of the viscoelastic sandwich structure. The proposed method is applied to the connecting bolt looseness condition detection of the viscoelastic sandwich structure to validate its effectiveness. Compared with the detection method based on dual-tree complex wavelet packet transform and energy and the detection method based on dual-tree complex wavelet packet transform and permutation entropy, the results indicate that the effectiveness of the proposed method in this article is more superior to that of the other two methods.

Uncertainty propagation in modal analysis of viscoelastic sandwich structures using a stochastic collocation method

This paper presents an uncertainty propagation analysis of viscoelastic sandwich laminated structures using a stochastic collocation method based on Smolyak’s sparse grids approach. Shear deformation in the viscoelastic layers is accurately captured by means of layer-wise plate finite elements. Modal properties are determined by solving nonlinear eigenvalue problems due to the frequency-dependent nature of the considered viscoelastic materials. Variations in material properties and layer thicknesses are both treated in a probabilistic way. The variabilities of natural frequencies and modal loss factors of two different viscoelastic laminated plate structures (plane and cylindrical) are studied. Results show that the present method converges quickly with small sample sizes when compared to the classical Monte-Carlo method. The influence of the variability of several parameters on viscoelastic structure modal properties is commented.

A novel aging state recognition method of a viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine

A viscoelastic sandwich structure is widely used in mechanical equipment, but therein viscoelastic layers inevitably suffer from aging which changes the dynamic characteristics of the structure and influences the whole performance of the equipment. Hence, accurate and automatic aging state recognition of the viscoelastic sandwich structure is very significant to monitor structural health state and guarantee equipment operating reliably. To fulfill this task, by analyzing the sensor-based vibration response signals, a novel aging state recognition approach of the viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine is proposed in this article. To extract effective aging feature information, the measured nonlinear and non-stationary vibration response signals are processed by dual-tree complex wavelet packet transform, and multiple permutation entropy features are extracted from the frequency-band signals to reflect structural aging states. For accurate and automatic aging state classification, generalized Chebyshev kernel is introduced, and multi-class generalized Chebyshev support vector machine is developed to classify structural aging states. In order to demonstrate the effectiveness of the proposed method, a typical viscoelastic sandwich structure is designed and fabricated, and various structural aging states are created through the hot oxygen–accelerated aging of viscoelastic layers. The testing results show that the proposed method can recognize the different structural aging states accurately and automatically. In addition, the superiority of dual-tree complex wavelet packet transform in processing vibration response signals and the performance of generalized Chebyshev support vector machine in classifying structural aging states are respectively validated by comparing with the commonly used methods.

Aging condition identification of viscoelastic sandwich structure based on empirical wavelet transform and Hilbert envelope demodulation

Viscoelastic sandwich structures (VSSs) have been widely used in mechanicalequipment. The aging condition monitoring of VSSs is essential for fault prevention of mechanical equipment. However, the ageing condition identification of VSS is still a challenging task since the change of structural response caused by the ageing of viscoelastic sandwich structure (VSS) is very puny. Therefore, a novel identification method based on empirical wavelet transform (EWT) and Hilbert envelope demodulation is proposed for the ageing condition identification of VSS. First, EWT is constructed to decompose the vibration response signals of VSS into a set of sub-band signals, so that the aging condition feature components are allocated in a certain frequency band. Second, the sub-band signals that contain rich aging condition information are selected and used to reconstruct a new signal. Thirdly, the reconstructed signal is analyzed with Hilbert envelopedemodulation to obtain an envelope spectrum. Finally, the low band energy of envelope spectrum is calculated as detection index to identify the aging condition of VSS. The proposed method is verified with a well-designed VSS in which the viscoelastic core subjected to accelerated ageing in a thermal-oxygen ambient. The results show the outstanding performance of the proposed method.

Aging State Detection of Viscoelastic Sandwich Structure Based on ELMD and Sensitive IA Spectrum Entropy

Viscoelastic sandwich structure is playing an important role in mechanical equipment, but therein viscoelastic material inevitably suffers from aging which affects structural service performance and the whole performance of equipment. Therefore, the aging state detection of viscoelastic sandwich structure based on vibration response signal is essential for monitoring the health state of structure and guaranteeing the operation safety of equipment. However, the weakness of structural vibration response variation caused by material aging make this task challenging. In this paper, a novel method based on ensemble local mean decomposition (ELMD) and sensitive IA spectrum entropy is proposed for this task. As an adaptive nonlinear and non-stationary signal processing method, ELMD is introduced to decompose the structural vibration response signal, and a series of instantaneous amplitudes (IAs) are obtained. Then, the spectrum entropies of these IAs are developed to quantitatively assess the aging state of viscoelastic sandwich structure. However, the IA spectrum entropies have different sensitivities to the aging state. Therefore, the most sensitive IA spectrum entropy is selected with a distance evaluation technique to detect the aging state of viscoelastic sandwich structure. In order to demonstrate the effectiveness of the proposed method, the experimental device of a viscoelastic sandwich structure is designed, and different structural aging states are created through the accelerated aging of viscoelastic material. The results show the outstanding performance of the proposed method.

A compression shear mixed finite element model for vibration and damping analysis of viscoelastic sandwich structures

A finite element model is developed to investigate the vibration and damping of elastic–viscoelastic–elastic sandwich beams. Two energy dissipation mechanisms, namely the shear and compression damping, are combined in the finite element model. Numerical examples are provided to verify the finite element model. The vibration and damping characteristics of the viscoelastic sandwich beams are investigated in detail. The numerical results show that the present finite element model has a good accuracy in predicting the natural frequencies and loss factors of viscoelastic sandwich beam structures. Moreover, it shows good applicability for both the thin and relatively thick sandwich beams. Its comprehensive performance is much better than the traditional shear and compression damping models. The effect of the viscoelastic material and geometrical parameters on the natural frequencies and loss factors of elastic–viscoelastic–elastic sandwich beam is studied as well. The results obtained have some significance in engineering application.

An identification method for frequency dependent material properties of viscoelastic sandwich structures

In this paper, an inverse identification method of the viscoelastic material properties (shear modulus G0 and loss factor ηC) of a symmetric three layered viscoelastic sandwich beam is proposed. Experimental vibration tests are performed to determine resonant frequencies and loss factors for different bending modes. The inverse approach consists into an iterative procedure that determines the mode shapes given the material parameters and then computes the viscoelastic properties from the modes using a Rayleigh quotient until convergence on the material properties is met. As a result, the frequency dependent viscoelastic material properties of sandwich beams are determined in an automated fashion. The method is successfully compared to the Ross-Kerwin-Ungar formulas and to a standard optimization approach. A fit of the viscoelastic material properties is performed providing analytical expressions for these quantities over a wide range of frequencies.

Improved layer-wise optimization algorithm for the design of viscoelastic composite structures

To combine lightness and rigidity in passive damping, elastic faces of visco-elastic sandwich structures are often made of laminates. These laminates are usually cross-ply, angle-ply, special orthotropic, anti-symmetric or balanced laminates which are commonly called classical laminates. In the design of visco-elastic sandwich structures, one often seeks to maximize the loss factor of the structure and its rigidity. To achieve this, computations are often made for several combinations of laminate fibers’ orientation angles. In this paper, the optimal design of composite laminates regarding the orientation angles is carried out by an improved layer-wise optimization algorithm (ILOA) by coupling a parametric non-linear eigenvalue problem resolution method (PANLER) with the so-called layer-wise optimization algorithm (LOA) to determine maximal frequencies and loss factors. The results are checked against a classical optimization algorithm.

Magneto-dynamic analysis of sandwiches composed of a thin viscoelastic-magnetorheological layer

This study’s relevance lies in the possibility of obtaining a smart sandwich structure with controllable dynamic properties, without compromising the main characteristics of conventional thin viscoelastic sandwiches. The proposed sandwich is composed of a micron-size viscoelastic-magnetorheological layer, which is obtained by adding spherical iron particles to a viscoelastic layer. Thin sandwiches with identical base layers and a viscoelastic core, with and without magnetic particles, are fabricated. Both the viscoelastic sandwich structure and viscoelastic-magnetorheological sandwich structure are experimentally characterised by a forced vibration test with resonance in a bandwidth between 0 and 1 kHz. This work analyses the influence of adding iron particles to obtain a smart thin sandwich. In addition, the vibrational response of the viscoelastic-magnetorheological sandwich structure under an external partial magnetic field, and the controllability of its dynamic properties by means of the magnetic field intensity and position, are studied.

Numerical method for nonlinear complex eigenvalues problems depending on two parameters: Application to three-layered viscoelastic composite structures

ABSTRACTThe design of high-damping composite viscoelastic sandwich structures (CVSS) requires continuous study of damping properties according to some parameters. The common design parameters that directly affect the damping properties of CVSS are the fibers' orientation and the thicknesses of the composite layers. However, until now there has been no method for continuously studying the influences of any design parameter on the damping properties of CVSS. Also, to study the influence of a design parameter on the damping properties of CVSS, computations are made for various discrete values of this parameter. These calculations are qualified incrementally, and are very expensive in computation time and do not allow to follow continuously the effects of the parameter on the damping properties of CVSS. In order to tackle these problems, we propose in this article a numerical method for studying the damping properties variation of CVSS according to a chosen modeling parameter, through the resolution of a generic, residual, complex nonlinear eigenvalues problems having frequency dependence and a modeling parameter that describes a study interval. This method is based on the asymptotic numerical method, automatic differentiation, homotopy technique and continuation. An application is performed to study the variation of the damping properties of a three-layered symmetric viscoelastic sandwich plates according to the fibers orientation of their orthotropic faces layers.

A nondeterministic GHM based model applied to sandwich beams

In order to obtain computational models to aid the design of viscoelastic sandwich structures used as passive damping treatment, many aspects should be considered. A crucial aspect is the incertitude inherent to experimental characterization tests of the complex modulus. Usually the computational model parameters are determined by curve fitting mean values of experimental dispersions. Considering that these dispersions are inherent to experimental tests and also those small variations in the fitted parameters lead to considerable changes on the dynamic behavior of a Viscoelastic Material (VEM), hence a nondeterministic model is more suitable than the usual deterministic ones. In that way, starting from dynamic properties of a VEM, a nondeterministic numerical model, which takes into account incertitudes in the VEM curve fitting procedure, is proposed.

A novel method to aging state recognition of viscoelastic sandwich structures

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, nonstationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

Comparison of non-linear eigensolvers for modal analysis of frequency dependent laminated visco-elastic sandwich plates

Modal analysis of frequency dependent visco-elastic sandwich structures with finite element models leads to a non-linear eigenvalue problem. Many methods have been proposed so far to solve this problem. The present paper compares the asymptotic numerical method to some representative algorithms such as non-linear Arnoldi method, non-linear Jacobi–Davidson method, inverse iteration and iterative shift-invert method. Three mesh refinement levels 1 (~5000 dofs), 2 (~20000 dofs) and 3 (~45000 dofs) have been used. Computational times and accuracy are compared on relevant use cases with frequency dependent visco-elastic materials. Three layered laminated sandwich plates with visco-elastic layers are considered as test cases and two different frequency and temperature dependent materials (ISD112 and PVB) are employed. It is shown that the non-linear Arnoldi method provides the best results in terms of accuracy and CPU time, inverse iteration being the most efficient method for small size problems. Moreover, it is shown that fractional derivative models commonly used to model frequency dependent viscoelastic materials are more CPU intensive than generalized Maxwell models when performing modal analysis of frequency dependent viscoelastic sandwich structures.