Xiwang Cui

Leakage accidents in gas pipelines not only result in significant property losses but also pose severe safety hazards. Thus, reliable leak localization technology is of great importance. Gas pipeline leak localization based on single-sensor modal acoustic emission and joint time–frequency analysis of Gabor-Smoothed Pseudo Wigner-Ville Distribution (Gabor-SPWVD) is proposed. First, an acoustic wave dispersion model is established based on the pipeline guided wave theory. The target modes and their feature frequency bands are identified through modal analysis. Then, a joint time–frequency analysis based on Gabor-SPWVD is performed and the time difference of arrival of the L (0,1) and F (1,1) modes is extracted from the time–frequency images. Finally, a single-sensor multimodal leak localization method is achieved based on the dispersion curves, group velocities, and time difference of different target modes. In the simulations, by comparing the modal time–frequency images of different pipe diameter and wall thickness, the results show that the time–frequency characterization capability of the proposed method is superior to that of the traditional methods. To verify the effectiveness of the proposed method, experiments were carried out. The experiments compared the single sensor multimodal localization performance under the conditions of different pipeline pressures and propagation distances. The experimental results show that the proposed method can effectively improve the time–frequency analysis capability and suppress the influence of cross interference terms, so as to accurately extract the time difference of arrival of the L (0,1) and F (1,1) modes for leak localization. The localization error of the proposed method is less than 6%.

To establish a lightweight yet highly discriminative diagnostic framework for reliable leak state identification using minimal signal data without relying on complex deep learning architectures, this paper proposes a novel approach integrating multiscale information entropy (MIE) and angle-distance-based supervised manifold mapping (SMM). The primary objective is to enhance the separability of high-dimensional acoustic features, particularly for short-duration signals. The method operates in two stages: MIE extracts entropy features across multiple time scales to capture cross-scale signal complexity, while SMM collaboratively optimizes intra-class compactness and inter-class separability, projecting features into a low-dimensional space with maximized discriminative power. This synergy directly addresses the common issues of insufficient cross-scale information capture and excessive model complexity in existing approaches. To validate the method, a pipeline leak experiment was conducted under no-leak (0 mm) and various leak aperture (1–6 mm) conditions. Using a Bayesian classifier with signal durations as short as 1 s, the proposed method was evaluated against multiple dimensionality reduction techniques. Results demonstrate clear separation of leak and non-leak samples in the three-dimensional SMM space, enabling intuitive visualization-based diagnosis without complex classifiers. The method maintains stable diagnostic performance even with 1-s signals, proving its ability to extract meaningful features from limited data, and achieves average recognition accuracy consistently exceeding 90% across all tested conditions—outperforming alternative methods. This study provides an efficient, interpretable, and high-performance solution for pipeline leak diagnosis, particularly well-suited for applications requiring reliable diagnosis from very short signal segments in data-limited scenarios.

Leakage accidents in natural gas pipelines bring huge property losses and pose serious safety risks. Therefore, faster and more accurate leakage localization is of great significance. In this paper, a new method based on modal acoustic emission (MAE) and peak optimization of cross-power spectrum is proposed to enhance the performance of leakage localization. When a pipeline leakage occurs, the sensors installed on the pipelines receive the acoustic emission (AE) signals, which have various modes. Instead of extracting a single mode through complex algorithms, the proposed method directly extracts the cross-power spectrum of a specified mode by choosing the appropriate window function and window parameters to locate the leakage with velocity information from dispersion curves. Gaussian, Hamming and Blackman window functions are compared in the extraction of the cross-power spectrum of L(0,1) mode, among which the Blackman window function is found to be the most effective. The artificial fish swarm algorithm combined with the iteration criterion for peak optimization is used to obtain suitable window parameters. Finally, the localization error of the above method is less than 5.2%. In addition, the performance of the iteration criterion for peak optimization of cross-power spectrum combined with other intelligent swarm algorithms are investigated. The results show that the maximum errors of other intelligent methods are still less than 9%, demonstrating the great compatibility of the proposed method.