This study is interested in how to utilise the virtual time reversal (VTR) technique to locate the continuous AE source in cylindrical shell structure. Thus, a six-AE-sensor monitoring network was designed to divide cylindrical surface into two symmetric semi-cylindrical monitoring areas whose four vertexes and two geometrical centres were defined as the measuring points. The AE source can be located in certain semi-cylindrical monitoring area whose centre measuring point first captured the emitted AE signal. Besides, the response signals at other four measuring points were used to localise the AE source. To realise the VTR focusing of the AE signals, the proposed method solved two key problems. One is to search the shortest helical path between two arbitrary points on a cylindrical surface, which can provide the shortest distance from AE source to sensor. The other is to extract the specific frequency coefficients of wavelet transform for the continuous AE signal, which can meet with the requirement for the instantaneous abrupt characteristic of the signals to be processed using VTR focusing technique. Finally, a continuous leak experiment was carried out on a gas-filled steel pipe. The corresponding localisation method can accurately estimate the location of the continuous leak source.

The small leak in the propulsion system pipeline of the sounding rocket is prone to occur in the connections because of the screw thread loosening. Due to economic and technical bottleneck, the traditional soap bubble method is widely used in practice to evaluate whether existing a leak or not by visually observing the bubble’s size and numbers. Thus doing so will result in the low inspection efficiency and high cost. Using acoustic emission (AE) techniques, this paper presents an experimental study on small leak detection on the screw thread connection in the propulsion system pipeline of sounding rocket. The time and frequency characteristics of the corresponding small leak AE signals are investigated. After characteristic indices extraction and selection, the multi-class support vector machines (MCSVM)-based leak rates recognition algorithm in One–vs–All (OVA) is proposed. It has been validated that, for the propulsion system pipeline of the sounding rocket, the dominant characteristic frequency band of the small leak AE signals induced by screw thread loosening concentrates on 35-45 kHz. The proposed optimal OVA SVM models can achieve good classification accuracy of >98% by using the characteristic index set {Envelope area, standard deviation (STD), root-mean-square (RMS), Energy, Average frequency} and Gaussian Radial Basis Function (RBF) kernel function. The drastic drops in the false alarm attribute to use the combination of time- and frequency-domain characteristic indices. Especially, once adding the “Envelope area” into the characteristic index set, the classification accuracies of the OVA SVM models are further improved significantly regardless of the effect of kernel functions.

This study is investigates how to reconstruct the continuous leak acoustic emission (AE) signal to precisely locate the leak source on a gas-filled steel pipe based on cross-correlation location theory. Thus, in this article, the determination method for the optimum mother wavelet was proposed. The frequency distribution of the continuous leak AE event was firstly extracted through wavelet transform (WT) with optimum mother wavelet. In the considered frequency range, the WT coefficients were further used to reconstruct the raw continuous leak AE signal. The correlation-based location experiments were finally carried out to evaluate the performance of the proposed method. The results show that the location errors of the leak source were decreased by some 10 % compared with those achieved by only using the raw AE signals. Thus, improvement verifies the validity of choice of the optimum mother wavelet. Meanwhile, it is also founded that, whether the reconstructed signals or raw signals were used, the localization accuracy of the leak source using the speed of the T wave is greater than that using the speed of the L wave and theoretical acoustic speed under the same symmetry of the AE sensors with reference to the leak source. Therefore, defining the acoustic speed with the speed of the T wave was recommended to use on-site for detection of the continuous leak source on a gas-filled steel pipe.

The time reversal focusing and imaging technique is often used to locate the acoustic emission (AE) source on a plate-type structure. Because of the computational error caused by the dispersive modes of the raw AE signal, the AE source is sometimes located in the adjacent region of its real position and the pseudo-AE source will be focused in the location image of the AE source. This study is interested in how to effectively avoid the focusing of the pseudo-AE source. Thus, in this article, a Shannon wavelet transform with “Morlet” mother wavelet was used to isolate S0 modes from raw AE signals. The modulus value curves of the isolated S0 modes were treated by time reversal, time delay, and superposition at every grid point in the monitoring area, based on the time reversal focusing principle. The AE source was located at a certain grid point where the peak of the superposition curve is maximal. The monitoring area imaging was realized by defining every grid point’s pixel value with the peak of the superposition curves. The validating experiment was done in a 600 mm by 600 mm monitoring area which is on a 5052 aluminum alloy plate with dimensions of 1,200 mm by 1,200 mm by 3 mm. Comparing with the traditional square location method based on time difference of arrival (TDOA) calculation, the localization accuracy was improved and the maximal radial error is lower than 30 mm. Furthermore, the proposed localization method has a certain ability to overcome the effect of the plate size and local curvature.

Steel pipes play a vital role in energy and industrial transportation systems, where undetected defects such as cracks and wall thinning may lead to severe safety hazards. Although ultrasonic guided waves enable long-range inspection, their defect imaging performance is often limited by dispersion, multimode interference, and strong noise. In this work, a multi-frequency fusion imaging method integrating time-reversal, topological derivative, and sparse Bayesian learning is proposed for guided wave-based defect detection in steel pipes. Multi-frequency guided waves are employed to enhance defect sensitivity and suppress frequency-dependent ambiguity. Time-reversal focusing is used to concentrate scattered energy at defect locations, while the topological derivative provides a global sensitivity map as physics-guided prior information. These results are further fused within a sparse Bayesian learning framework to achieve probabilistic defect imaging and uncertainty quantification. Dispersion compensation based on the semi-analytical finite element method is introduced to ensure accurate wavefield reconstruction at different frequencies. Domain randomization is also incorporated to improve robustness against uncertainties in material properties, temperature, and measurement noise. Numerical simulation results verify that the proposed method achieves high localization accuracy and significantly outperforms conventional TR-based imaging in terms of resolution, false alarm suppression, and stability. The proposed approach provides a reliable and robust solution for guided wave inspection of steel pipelines and offers strong potential for engineering applications in nondestructive evaluation and structural health monitoring.