Imaging microdamage, represented by the fatigue cracks, presents a significant challenge, yet is crucial for ensuring structural integrity in industrial applications. The development of nonlinear ultrasonic Lamb wave techniques provides a practical avenue for fatigue crack imaging, but the inherently weak nonlinear features hinder the practical application. Sparse representation method paves a promising way for reconstructing the weak second harmonic component, enabling the fatigue crack imaging. However, existing sparse representation method used in the nonlinear ultrasonic research only focuses on the second harmonic component reconstruction, neglecting the fundamental component, which accounts for most of the wave energy. This oversight leads to an underestimation of the second harmonic component and the potential generation of artifact images. To address this limitation, a framework for simultaneously reconstructing the second harmonic and fundamental components from original waves, enabling micro- and macro-damage imaging, combining with the nonlinear ultrasonic Lamb wave technique is proposed in this work. The acoustic linearity- and nonlinearity-aware dictionaries, based on the wave propagation model, are designed to effectively separate the fundamental and second harmonic components. The split augmented Lagrangian shrinkage algorithm is employed to reconstruct both components, enabling imaging of macro-damage using the fundamental component and enhanced fatigue crack imaging based on the second harmonic component. Numerical simulation and experimental validation highlight the framework’s performance, achieving both fatigue crack imaging and artifact suppression.

The visualization of the microcrack growth is vital for structural integrity evaluation and remaining useful life prediction in the industrial field, but there is a scarcity of practical and low-cost techniques today to detect and image microcracks at the early stage of damage. A novel framework for visualizing fatigue crack is developed, integrating the nonlinear ultrasonic Lamb wave phased array technology with the sparse representation algorithm. A designed nonlinear phased array is utilized in conjunction with the introduction of pulse inversion technology for enhancing weak nonlinear ultrasonic features. Based on the inherent dispersion nature of Lamb waves and second harmonic propagation features, a kind of acoustic nonlinearity aware-dictionary is designed, and the gradient projection sparse representation algorithm is employed to reconstruct the weak second harmonic waves embedded in the original time domain signals. The experimental validation, derived from the imaging result of the fatigue crack at different fatigue cycles and the fatigue crack growth path visualization under structural health monitoring conditions, demonstrates the capability of the developed framework to extract the weak nonlinear features generated by the fatigue crack. Some essential parameters can be calculated based on the obtained growth path, thus laying the foundation for future assessment of structural integrity using fracture mechanics theory.

The defect classification task is of great benefit to evaluating the safety performance of equipment and providing useful feedback information for discovering production process problems. In this article, we present a semisupervised learning (SSL) framework for transient thermography detection to employ the temporal and spatial information encoded into the three-dimensional transient thermal tensor data and provide pixel-level classification results for defect types. The time- and frequency-domain physical models for the transient thermal evolution of different kinds of defects are established to illustrate the theoretical foundation of defects classification based on transient thermography. The semisupervised multiclass Laplacian support vector machine is proposed to enable involving the abundant unlabeled data for enhancing learning performance in practical industrial applications where labeled samples are insufficient and labeling work is costly and laborious. A case study on silicone insulating materials with various types of artificial simulated internal defects validates the stronger generalized ability of the proposed method. This work, for the first time, proposes an SSL framework in transient thermography-based defect detection studies. It is believed that our proposed method is quite inspired for introducing SSL techniques to transient thermography for preferable performance in practical industrial applications.

As an increasingly recognized optical interferometric technique for nondestructive testing and evaluation, shearography has attracted extensive interest in various industrial applications. However, it suffers from the ignorance of the whole process of dynamic surface deformation and the difficulty in determining the depth-resolved information of inhomogeneities. In this regard, a novel speckle pattern interferometric modality named photothermal radar shearography is proposed. Unlike the differential mode of conventional shearography, the present article focuses on the dynamic surface displacement field channel technique with more comprehensive understanding of the subsurface structural information. With the utilization of frequency modulated photothermal excitation, the depth-distributed information of subsurface structures and inhomogeneities is encoded into the induced dynamic surface deformation. Using Hilbert transform and least-square method solved by discrete cosine transform, the time-domain interference signal of each pixel from the recorded speckle patterns sequence is demodulated and unwrapped to obtain the transient full-field shearographic phase distribution which indicates the dynamic surface deformation. In the meanwhile, incrementally delayed cross-correlation matched filtering allows for the localization of axial energy distribution to generate depth-selective structural display for tomography. This proposed transient-based interferometric methodology thus enables depth-tomographic profiles and three-dimensional visualization of subsurface anomalies for the first time, which significantly improves the superiority and attractiveness of shearography in providing insight into the status, performance and reliability of industry.

Pollution flashover is a serious accident with a wide range of impacts in power grid. Accurate and timely classification of pollution severity is both a key to preventing pollution flashovers and a crucial challenge. This article proposes an innovative method based on photothermal radiometry (PTR) and multiclass semisupervised support vector machine for the classification of insulator pollution severity. The introduction of time-dimension information makes it possible to achieve pixel-level image identification instead of image- or region-level image identification. The PTR physical model for pollution severity measurement is established to determine the effect of pollution severity parameters such as the equivalent salt deposit density and the nonsoluble deposit density on the transient and frequency-domain thermal radiation characteristics of contamination layer. The relevant features are extracted by using principal component analysis. A semisupervised classifier is proposed to solve the problem of poor generalization due to insufficient labeled samples in industrial applications. Experimental results verify the satisfactory efficiency and accuracy of the proposed method and an estimation framework for fast, accurate, and nondestructive industrial application without the tedious work of labeling large amounts of data is concluded.

This article introduces differential evolution fitting-based optical step-phase thermography for fast, contactless, and high-accurate measurement of micrometer thickness, which is widely needed in industrial applications. In this process, frequency-domain characteristics of thermal wave propagation are extracted to estimate micrometer thickness. The quantitative relationships between the frequency-domain thermal characteristics and different thicknesses of micrometer structures are precisely discussed. The effect of sampling frequency is emphasized for measuring micrometer thickness and improving the sensitivity of the frequency-domain features as measurement tools of thickness. In particular, differential evolution is introduced to implement adaptive global optimization of transient thermal responses fitting and to eliminate the frequency aliasing in micrometer thickness measurement when the sampling frequency is not satisfactory. Finally, experimental studies have been conducted to compare the effectiveness of principle component analysis, independent component analysis, and the proposed method for quantitative evaluation of atmospheric corrosion. It can be seen that the frequency-domain features imaging is capable of quantitatively characterizing micrometer thickness in a visualized way, and the influences of abnormal surface states and nonuniform heating are completely eliminated.