This paper presents a new Acoustic Emission (AE) source location method in combination with a Wigner-Ville Distribution (WVD) and a theoretical velocity dispersion model in dispersive rock. This method does not require measuring the first arrival time of a signal, and is able to provide reliable and identical Time-of-Arrival-Differences (TOADs) regardless of the frequency components in the obtained signals. The basic idea behind this is that, if at least two dominant frequency components in the output signals can be determined from the WVD analysis of the test results, a full wave dispersion curve of the arriving signal is obtained in accordance with the theoretical wave model, where the time differences between dispersion curves are identical through the entire effective frequency range of the AE transducers. Biot’s model was applied as a velocity dispersion model, and the smoothed pseudo WVD was used to reduce the cross-term interference. It was found that the frequency-dependent arrival time traveling with the group velocity was easily determined with the help of the theoretical model, and the source location results based on the WVD algorithm and theoretical model agree better with the true locations than those from the Time-of-First-Arrival Differences (TOFADs).

An elbow wall thinning diagnosis method by highlighting the stationary characteristics of the operating loop is proposed. The accelerations of curved pipe surfaces were measured in a closed test loop operating at a constant pump rpm, combined with curved pipe specimens with artificial wall thinning. The vibration characteristics of wall-thinned elbows were extracted by using a mel-spectrogram in which modal characteristic variation shifting can be expressed. To reduce the deviation of the model’s prediction values, the ensemble mean value of the mel-spectrogram was used to emphasize stationary signals and reduce noise signals. A convolutional neural network (CNN) regression model with residual blocks was proposed and showed improved performance compared to the models without the residual block. The proposed regression model predicted the thinning thickness of the elbow excluded in training dataset.

This study presents a method to add a crack analysis algorithm to the Acoustic Leak Monitoring System (ALMS) to detect and evaluate the crack growth process in the primary system piping of nuclear power plants. To achieve this, a fracture test was conducted by applying stepwise loading to welded specimens that simulate the cold leg section, and acoustic emission (AE) signals were measured in relation to the increase in strain using an AE testing system. The experimental results indicated that the stability and instability of cracks could be assessed through the Kaiser effect and the Felicity effect when detecting crack growth using AE signals. Additionally, by utilizing both root mean square (RMS) and amplitude parameters simultaneously to calculate the b-value, it was confirmed that the RMS-based b-value minimizes the effects of AE signal attenuation and allows for a more stable assessment of crack progression. This demonstrates that the RMS, which reflects signal energy, is effective for real-time monitoring of the crack growth state. Finally, the results of this study suggest the potential for real-time crack monitoring using AE data in piping systems of critical structures, such as nuclear power plants; by adding a simple AE analysis method to the ALMS system, a practical approach has been derived that enhances the safety of the structure and allows for quantitative assessment of crack progression. Future research is expected to further refine the AE parameters and algorithms, leading to the advancement of safety monitoring systems in various industrial settings.

A pipe wall thinning diagnosis method based on vibration characteristics is proposed. Elbow specimens with artificial pipe wall thinning were fabricated and combined in a loop. By running a pump in the loop, vibration was induced by flow, and the vibrational signals were measured with accelerometers. The effect of pipe wall thinning on the vibrational signals was investigated by analyzing the spectral data of the acceleration signals. The analyzed vibration characteristics were difficult to observe because the change in characteristics was small. A convolutional neural network (CNN) specialized for data recognition was applied to recognize the small change in vibrational signal resulting from the pipe wall thinning. A regression model based on CNN was chosen to learn the tendency of change in the vibrational signals with varying thinning. The data types advantageous for training the regression model were identified. An early stopping technique using the validation data set was adopted to regularize the regression model. The trained regression model was able to predict pipe thinning.

Acoustic emissions (AE) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNR(signal-to-noise ratios) generated in rocks. The basic idea comes from that the variances of the crack wave and noise have different characteristics depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The results using the proposed algorithm to detect crack wave source localization suggest use for evaluating and real-time monitoring of damage in tunnels or other underground facilities.