Lined anti-corrosion pipes are widely used in oil and gas, petrochemical, pharmaceutical industries. However, defects, especially delamination, may occur in the production and service of pipes which result in safety accidents. Based on nonlinear ultrasonic theory, this paper studied the delamination detection method using the nonlinear harmonics for lined anti-corrosion pipes. The response characteristics of the anti-corrosion pipe were obtained through a sweep sine response experiment and the preferred excitation frequency was determined. The Wavelet Packet transform and Hilbert–Huang transform is applied for signal process and feature extraction. Then, a series of experiments were carried out and the results were analyzed and discussed. The results showed that a second-order and third-order nonlinear coefficient increased with the delamination damage. The amplitude of second-harmonic is much stronger than the third-order one. The mean squared error of the nonlinear coefficient, which was processed by Wavelet Packet transform and Hilbert–Huang transform, is smaller than wavelet packet transform and Discrete Fourier transform or processed only Hilbert–Huang transform. The higher harmonics can describe the change of delamination damage, which means that the nonlinear ultrasonic detection method could use for damage detection of anti-corrosion pipe. The nonlinear higher-harmonic is sensitive to delamination damage. The nonlinear ultrasonic method has the potential for damage detection for lined anti-corrosion pipes.

Debonding defects are common and they are the main reason for the failure of hidden frame supported glass curtain walls, which are widely used as an external enclosure and decorative structure. In this paper, a debonding detection method for hidden frame supported glass curtain walls is developed based on nonlinear ultrasonic modulation and piezoceramic transducers. First, the excitation frequency was determined according to the response characteristics. Then, empirical mode decomposition (EMD) was applied to extract the feature components. After discrete Fourier transform (DFT), the nonlinear coefficients were calculated to evaluate the debonding defect. Finally, the experimental setup was established and a series of experiments were carried out to verify the feasibility and effectiveness of the nonlinear ultrasonic modulation method. The nonlinear harmonics detection method was also investigated and it was compared with the nonlinear ultrasonic modulation method. The detection effect at different temperatures and impact were studied. The results showed that the nonlinear coefficient increases with the debonding length. The mean squared error (MSE) of the nonlinear ultrasonic modulation method was improved by 41% compared with the nonlinear harmonics method. The nonlinear ultrasonic modulation method can successfully detect debonding defects in hidden frame supported glass curtain walls at different temperatures and impact.

Guided wave imaging can obtain the damage characterization of imaging features and time difference, and has become a promising tool for structural health monitoring. But most current imaging algorithms still manually select imaging features from first wave or scatter signal. Such manual feature extraction method highly depends on the selection criterion and would significantly reduce the generalization of the monitoring model. In this article, a deep convolutional neural network probability imaging algorithm (DCNN-PIA) is proposed to provide an automatic high-level damage index extraction method for guided wave imaging. Feature selection and multisensor imbalance can be turned away in this semisupervised deep learning method, and the damage will be presented visually. The experiment results illustrate that the proposed method can detect the damage only by using normal state signals, presents a good materials generalization in both aluminum plate and composite plate, and has better performance than other state-of-the-art methods.

Contact delamination damage easily occurs inside the composite. The extension and accumulation of the damage occupy most of the time from damage appearance to failure. However, the anisotropy of materials and the small size of early damage make accurate detection difficult. In this paper, a non-elliptical probability imaging (NEPI) method based on nonlinear ultrasonic guided waves is proposed for the delamination damage detection in anisotropic composites. Firstly, the anisotropic characteristics of the composite are analyzed theoretically, and the velocity in all directions is obtained by numerical simulation. Secondly, the arrival time is obtained by the intersection of the upper and lower envelope fitting lines, and nonlinear coefficient is calculated based on the smooth pseudo Wigner–Ville distribution (SPWVD). Then, the time coefficient ( C_T) and nonlinear damage index (NDI) are defined and applied in the NEPI. By comparison with a reference point, the problem that the damage location of anisotropic composite cannot be solved analytically is avoided skillfully. Finally, the NEPI method is used to present the damage. The experiment results show that the NEPI method can accurately display the damage location. The sensitivity and reliability of NDI based on SPWVD are further verified by comparison with damage index of scattered signal and time–frequency analysis methods such as fast Fourier transform, short-time Fourier transform, and S-transform. The proposed NEPI method based on nonlinear ultrasonic guided waves can detect the delamination damage with good location accuracy and high damage sensitivity.

Degradation of interfacial adhesive strength is the main reason for failure of the hidden frame–supported glass curtain wall that is now widely used in the construction field. In this paper, a nonlinear ultrasonic Lamb wave detection method was developed for hidden frame–supported glass curtain walls. Both theoretical and experimental studies were carried out to verify the feasibility of the method. First, an excitation signal modulated by the Hanning window was generated through piezoceramic transducers. The artificial intervention of thermal aging was applied to accelerate the degradation of the interfacial adhesion strength, and the reflection signal of different aging periods was obtained. Then, two methods of discrete Fourier transform (DFT) and wavelet packet decomposition were used to feature extraction, and normalization and regression analysis were adopted to compare two methods more reasonably. The experiment results show that the amplitude integral method using the second-order relative nonlinear coefficient can reflect the interfacial adhesion strength sensitively, whereas the variation curve of the third-order relative nonlinear coefficient was not obvious. The result of the wavelet packet energy method was close to the amplitude integral method and consistent with reality. The second-order relative nonlinear coefficient fluctuated over a small range at first; then there was a decline, and it finally increased with the thermal aging time. However, the mean squared error (MSE) of the wavelet energy method was smaller. The wavelet packet energy method is more accurate and has a higher allowable error ability in characterizing the trends.