Thickness of thermal barrier coating (TBC) plays an important role in the insulation property of coating. Eddy current (EC) testing could be used to measure the thickness of ceramic coating (TC) for TBCs. However, previous EC testing has made it difficult to decouple the relationship between TC, bond coating (BC), and substrate. In this study, an accurate and simple method of TC thickness measurement is proposed based on the mutual inductance and the transformer model, which is immune to the BC and the substrate. First, an EC system is analyzed using the transformer model. Second, mutual inductance is formulated with the Neumann formula and the Taylor series, and the coupling coefficient is established according to the approximate and simplified mutual inductance expression. Third, the coupling relationship is revealed, and the combination of the change in inductance and the phase signature serves as the new feature to characterize the thickness of TC immune to the variation in BC and substrate. Finally, an EC detection system is designed, and experiments are carried out to evaluate the thickness of TC. The results show that the relative errors are less than 5% and the method has a good inhibition effect on the change in temperature.

Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances.

The de-bonding defects in the thermal barrier coatings (TBCs) are tiny and thin, yielding severely overlapped Terahertz (THz) echoes. Hence, we introduce a powerful signal processing method using a customized wavelet basis based wavelet transform. To begin with, an analytical model of THz signals in TBCs is constructed. Then, THz echoes and typically established wavelet basis are measured in similarity, and the Gaussian wavelet basis is selected to separate overlapped THz pulses due to the largest similarity. To further improve the performance of Gaussian wavelet transform, a novel firefly algorithm (FA) is proposed to search the optimal wavelet parameters. The superiority of the novel FA lies in dynamically adjusting step size by monitoring the change of attractiveness, allowing for jumping out of local optima. Finally, experiments were carried out, and extensive comparison were made with multiple reported methods. The results demonstrate that the proposed method enables high resolution and accuracy of THz imaging for de-bonding defects in TBCs when temporal resolution of THz time domain spectrometer is hardware limited.

Eddy current testing technology is widely used in the defect detection of metal components and the integrity evaluation of critical components. However, at present, the evaluation and analysis of defect signals are still mostly based on artificial evaluation. Therefore, the evaluation of defects is often subjectively affected by human factors, which may lead to a lack in objectivity, accuracy, and reliability. In this paper, the feature extraction of non-linear signals is carried out. First, using the kernel-based principal component analysis (KPCA) algorithm. Secondly, based on the feature vectors of defects, the classification of an extreme learning machine (ELM) for different defects is studied. Compared with traditional classifiers, such as artificial neural network (ANN) and support vector machine (SVM), the accuracy and rapidity of ELM are more advantageous. Based on the accurate classification of defects, the linear least-squares fitting is used to further quantitatively evaluate the defects. Finally, the experimental results have verified the effectiveness of the proposed method, which involves automatic defect classification and quantitative analysis.

In the actual production environment, the eddy current imaging inspection of titanium plate defects is prone to scan shift, scale distortion, and noise interference in varying degrees, which leads to the defect false detection and even missed inspection. In view of this problem, a novel image recognition and classification method based on convolutional neural network (CNN) for eddy current detection of titanium plate defects is proposed. By constructing a variety of experimental conditions and collecting defect signals, the characteristics of eddy current testing (ECT) signals for titanium plate defects are analyzed, and then the convolution structure and learning parameters are set. The structural characteristics of local connectivity and shared weights of CNN have better feature learning and characterization capabilities for titanium plate defect images under scan shift, scale distortion, and strong noise interference. The results prove that, compared with other deep learning and classical machine learning methods, the CNN has a higher recognition and classification accuracy for the defect eddy current image of the titanium plate in the complex detection environment.

Terahertz (THz) thickness measurements of thermal barrier coatings vary within a certain range in terms of accuracy and occasionally generate unsatisfying results. To identify such results, a physics-constrained transfer learning framework is presented to achieve self-assessment of measurement results. First, an advanced neural network is employed to estimate thickness, roughness, and refractive index, followed by inputting them into the analytical model to generate simulated traces, which are subtracted by the actual signals to obtain the residual data. Then, we find that the residuals of the first two peaks from experimental and simulated THz signals are similar parts, and they are correlated to the error through the phase factor in THz physics. Thus, a physics-constrained layer is presented to amplify the residuals of the first two peaks to improve the similarity. Finally, the trained physics-constrained transfer learning can achieve satisfactory accuracy for assessing the thickness measurement results of actual specimens.