Shearography and thermography are optical techniques, both proven to be valuable tools for material nondestructive evaluation. Papers on these topics, however, are scattered and mainly appeared in optical journals. For the convenience of the materials community, this paper aims to present a comprehensive review of shearography and active thermography and their applications in nondestructive evaluation of materials. Both techniques enjoy the merits of full-field, non-contact and allowing speedy detection of material defects in metal, non-metal as well as composites materials. However, they are fundamentally different in flaw detection mechanisms. Shearography measures materials’ mechanical response to stresses, whereas active thermography measures material's heat-transfer response to an instantaneous thermal excitation. A comparison of the advantages and limitations of two techniques for nondestructive evaluation will also be presented.

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Review and comparison of shearography and active thermography for nondestructive evaluation

We address the problem of in-network distributed estimation for sparse vectors In order to exploit the underlying sparsity of the vector of interest, we incorporate the l1- and l0-norm constraints into the cost function of the standard diffusion least-mean squares (LMS) This technique is equivalent to adding a zero-attracting term in the iteration of the LMS-based algorithm, which accelerates the convergence rates of the zero or near-zero components The rules for selecting the intensity of the zero-attracting term are derived and verified Simulation results show that the performances of the proposed schemes depend on the degree of sparsity Provided that suitable intensities of the zero-attracting term are selected, they can outperform the standard diffusion LMS when the considered vector is sparse In addition, a practical application of the proposed sparse algorithms in spectrum estimation for a narrow-band source is presented

Diffusion Sparse Least-Mean Squares Over Networks

As one of the recently proposed algorithms for sparse system identification, I0 norm constraint Least Mean Square (io-LMS) algorithm modifies the cost function of the traditional method with a penalty of tap-weight sparsity. The performance of I0-LMS is quite attractive compared with its various precursors. However, there has been no detailed study of its performance. This paper presents comprehensive theoretical performance analysis of I0-LMS for white Gaussian input data based on some reasonable assumptions, which are reasonable in a large range of parameter setting. Expressions for steady-state mean square deviation (MSD) are derived and discussed with respect to algorithm parameters and system sparsity. The parameter selection rule is established for achieving the best performance. Approximated with Taylor series, the instantaneous behavior is also derived. In addition, the relationship between I0-LMS and some previous arts and the sufficient conditions for I0-LMS to accelerate convergence are set up. Finally, all of the theoretical results are compared with simulations and are shown to agree well in a wide range of parameters.

Performance Analysis of $l_0$ Norm Constraint Least Mean Square Algorithm

Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation

In this letter, the RLS adaptive algorithm is considered in the system identification setting. The RLS algorithm is regularized using a general convex function of the system impulse response estimate. The normal equations corresponding to the convex regularized cost function are derived, and a recursive algorithm for the update of the tap estimates is established. We also introduce a closed-form expression for selecting the regularization parameter. With this selection of the regularization parameter, we show that the convex regularized RLS algorithm performs as well as, and possibly better than, the regular RLS when there is a constraint on the value of the convex function evaluated at the true weight vector. Simulations demonstrate the superiority of the convex regularized RLS with automatic parameter selection over regular RLS for the sparse system identification setting.

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RLS Algorithm With Convex Regularization

Sparsity property has long been exploited to improve the performance of least mean square (LMS) based identification of sparse systems, in the form of l0-norm or l1-norm constraint. However, there is a lack of theoretical investigations regarding the optimum norm constraint for specific system with different sparsity. This paper presents an approach by seeking the tradeoff between the sparsity exploitation effect of norm constraint and the estimation bias it produces, from which a novel algorithm is derived to modify the cost function of classic LMS algorithm with a non-uniform norm (p-norm like) penalty. This modification is equivalent to impose a sequence of l0-norm or l1-norm zero attraction elements on the iteration according to the relative value of each filter coefficient among all the entries. The superiorities of the proposed method including improved convergence rate as well as better tolerance upon different sparsity are demonstrated by numerical simulations.

Non-Uniform Norm Constraint LMS Algorithm for Sparse System Identification

The objective of this paper is to investigate the acoustics characteristics obtained from impact sounds excited by impact on tiled-walls and hence to develop a rapid, effective and robust non-destructive evaluation method for bonding integrity inspection. Most of the previous research on impact acoustics evaluation relies on the frequency-domain characteristics of the free-vibration component of impact sounds for detection and feature extraction. This method proves to be too sensitive to the surface roughness of the practical structures. In the present work, special attention is paid to the time-domain acoustics to present a solution tolerant of high surface non-uniformity. Based on the impact dynamics analysis, the relationship between the acceleration and ringing components of impact sounds and the corresponding bonding state is firstly clarified. Investigation performed on the pattern of the initial acceleration component in the time record reveals its effectiveness in indicating the existence of bonding defects in tile-walls with satisfactory immunity to surface roughness. By properly selecting the feature vectors extracted from the initial acceleration peaks of the impact sounds, a multilayer back-propagation artificial neural network (ANN) classifier is developed for automatic health monitoring of the tile-walls. The inspection results obtained experimentally on sample slabs are presented and discussed, confirming the validity of the proposed method.

Tile-wall bonding integrity inspection based on time-domain features of impact acoustics

Distributed compressed sensing estimation of underwater acoustic OFDM channel

Impact-acoustics-based health monitoring of tile-wall bonding integrity using principal component analysis

It is well recognized that the impact-acoustic emissions contain information that can indicate the presence of the adhesive defects in the bonding structures. In our previous papers, artificial neural network (ANN) was adopted to assess the bonding integrity of the tile–walls with the feature extracted from the power spectral density (PSD) of the impact-acoustic signals acting as the input of classifier. However, in addition to the inconvenience posed by the general drawbacks such as long training time and large number of training samples needed, the performance of the classic ANN classifier is deteriorated by the similar spectral characteristics between different bonding status caused by abnormal impacts. In this paper our previous works was developed by the employment of the least-squares support vector machine (LS-SVM) classifier instead of the ANN to derive a bonding integrity recognition approach with better reliability and enhanced immunity to surface roughness. With the help of the specially designed artificial sample slabs, experiments results obtained with the proposed method are provided and compared with that using the ANN classifier, demonstrating the effectiveness of the present strategy.

Feng Tong

Papers

Non-Uniform Norm Constraint LMS Algorithm for Sparse System Identification

Tile-wall bonding integrity inspection based on time-domain features of impact acoustics

Distributed compressed sensing estimation of underwater acoustic OFDM channel

Impact-acoustics-based health monitoring of tile-wall bonding integrity using principal component analysis

Evaluation of tile–wall bonding integrity based on impact acoustics and support vector machine