Sparse wavenumber analysis of guided wave based on hybrid Lasso regression in composite laminates

2021 ◽  
pp. 147592172110321
Author(s):  
Yue Hu ◽  
Fangsen Cui ◽  
Fucai Li ◽  
Xiaotong Tu ◽  
Liang Zeng
Keyword(s):  
Composite Laminates ◽  
Composite Laminate ◽  
Wave Attenuation ◽  
Dispersion Curves ◽  
Guided Wave ◽  
Propagation Mechanism ◽  
Lasso Regression ◽  
Group Sparsity ◽  
Guided Wave Propagation ◽  
Selection Operator

The guided wave is an efficient and reliable tool for the structural health monitoring (SHM) of the composite laminates. In the guided wave-based SHM methods, extracting the dispersion curves is essential for integrity evaluation. In this study, a sparse wavenumber analysis based on hybrid least absolute shrinkage and selection operator (Lasso) regression is proposed to extract the dispersion curves in the frequency–wavenumber distribution (FKD) for the composite laminate. The hybrid Lasso regression model is constructed based on the guided wave propagation mechanism. Considering that responses of some wave modes are very weak at specific frequencies due to the guided wave attenuation in the composite laminates, the group-sparsity and continuity regularizations are imposed in this model to improve frequency–wavenumber resolution and remove noises. Only few sensors are required for the proposed method to extract the dispersion curves. Both the simulation and the experiment are used to verify the effectiveness of the proposed method. Furthermore, the material property of the composite laminate in the experiment is non-destructively estimated by using the dispersion curves extracted by the proposed method.

Guided wave propagation in composite laminates using piezoelectric wafer active sensors

2013 ◽  
Vol 117 (1196) ◽  
pp. 971-995 ◽  
Author(s):  
M. Gresil ◽  
V. Giurgiutiu
Keyword(s):  
Wave Propagation ◽  
Composite Laminates ◽  
Composite Laminate ◽  
Mechanical Impedance ◽  
Guided Wave ◽  
Impedance Method ◽  
Active Sensors ◽  
Piezoelectric Wafer Active Sensors ◽  
Guided Wave Propagation ◽  
Piezoelectric Wafer

AbstractPiezoelectric wafer active sensors (PWAS) are lightweight and inexpensive transducers that enable a large class of structural health monitoring (SHM) applications such as: (a) embedded guided wave ultrasonics, i.e., pitch-catch, pulse-echo, phased arrays; (b) high-frequency modal sensing, i.e., electro-mechanical impedance method; and (c) passive detection. The focus of this paper is on the challenges posed by using PWAS transducers in the composite laminate structures as different from the metallic structures on which this methodology was initially developed. After a brief introduction, the paper reviews the PWAS-based SHM principles. It follows with a discussion of guided wave propagation in composites and PWAS tuning effects. Then, the mechanical effect is discussed on the integration of piezoelectric wafer inside the laminate using a compression after impact. Experiments were performed on a glass fibre laminate, employing PWAS to measure the attenuation coefficient. Finally, the paper presents some experimental and multi-physics finite element method (MP-FEM) results on guided wave propagation in composite laminate specimens.

scholarly journals Guided Wave Propagation for Monitoring the Rail Base

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guodong Yue ◽  
Xiushi Cui ◽  
Ke Zhang ◽  
Zhan Wang ◽  
Dong An
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Wave Attenuation ◽  
Guided Wave ◽  
The Finite Element Method ◽  
Bottom Edge ◽  
Propagation Process ◽  
Guided Wave Propagation ◽  
Propagation Properties ◽  
Element Method

In order to monitor the rail base, the dispersion characteristics and propagation properties of the guided wave are studied. Firstly, two modes named as Modes V1 and V2 are selected by the semianalytical finite element method (SAFE). The region at the bottom edge can be monitored by Mode V1, while the junction of the base edge and the flange can be detected by Mode V2. Then, the characteristics in the propagation process are analyzed using the finite element method (FEM). The two modes can be separated about 0.6 ms after they are excited. Thirdly, a wave attenuation algorithm based on mean is proposed to quantify the wave attenuation. Both waves can have weak attenuation and be detected within 5 m. Finally, a mode-identified experiment is performed to validate the aforementioned analysis. And a defect detection experiment is performed to demonstrate the excellent monitoring characteristics using Mode V2. These results can be used to monitor the rail base in practice engineering.

scholarly journals Phased Array Beamsteering in Composite Laminates for Guided Wave Structural Health Monitoring

2013 ◽  
Author(s):  
Peter Osterc ◽  
Daewon Kim ◽  
Byungseok Yoo
Keyword(s):  
Wave Propagation ◽  
Composite Laminates ◽  
Phased Array ◽  
Linear Array ◽  
Wave Mode ◽  
Guided Wave ◽  
Isotropic Case ◽  
Inherent Anisotropy ◽  
Guided Wave Propagation ◽  
And Performance

In this study, a guided wave phased array beamsteering approach is applied to composite laminates. Current beamsteering algorithms derived for isotropic materials assume omnidirectional wave propagation. Due to inherent anisotropy in composites, guided wave propagation varies with direction and wavefronts no longer have perfect circular shapes. By examining slowness, velocity and wave curves for a given composite laminate, the wavefront from a single source can be described as a function of the angle of propagation and distance from origin. Using this approach, a generic delay and sum beamforming algorithm for composite laminates is developed for any desired wave mode. It is shown that anisotropic wave mode regions can be effectively used for beamsteering in certain directions with a linear array and performance similar or even better than isotropic case. However, the useful range of angles with a 1d linear array for anisotropic wave modes is quite small and other directions exhibit undesired grating lobes and large sidelobes.

Investigation on fundamental modes of guided waves propagating in symmetric and nonsymmetric composite laminates

2017 ◽  
Vol 231 (16) ◽  
pp. 2988-3000 ◽  
Author(s):  
L Maio ◽  
V Memmolo ◽  
F Ricci ◽  
ND Boffa ◽  
E Monaco
Keyword(s):  
Composite Laminates ◽  
Guided Waves ◽  
Mechanical Response ◽  
Isotropic Plate ◽  
Three Dimensional ◽  
Composite Plates ◽  
Guided Wave ◽  
Membrane Behavior ◽  
Isotropic Composite ◽  
Guided Wave Propagation

A quasi-isotropic composite laminate is constructed in an attempt to create a structure that behaves like an isotropic plate. Its membrane behavior is similar to that of the isotropic plate while the bending behavior is quite different from the latter. Moreover, the laminae may or may not be arranged symmetrically with respect to the midplane thereby resulting in a different mechanical response. In this work, guided wave propagation along multiple directions in symmetric and not symmetric quasi-isotropic plates is evaluated. Experimental and numerical results for the fundamental modes A0 and S0 are analyzed for the symmetric and nonsymmetric layups. An eight-node brick type element based on the three-dimensional theory is used in modeling to predict numerically the velocity of wave modes propagating in the graphite/epoxy composite plates. Agreement between experimental and numerical approaches is found and interesting dependencies between velocity of propagating modes and laminate stacking sequence are discussed. A final comparison with analytical dispersion curves obtained by the implementation of the global matrix method is discussed.

A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

Manipulation of the guided wave propagation in multilayered phononic plates by introducing interface delaminations

2021 ◽  
pp. 104266
Author(s):  
Yanzheng Wang ◽  
Elias Perras ◽  
Mikhail V. Golub ◽  
Sergey I. Fomenko ◽  
Chuanzeng Zhang ◽  
...  
Keyword(s):  
Wave Propagation ◽  
Guided Wave ◽  
Guided Wave Propagation
Sign in / Sign up

Export Citation Format

Share Document