Abstract
In recent years, acoustic emission (AE) has been widely used in pipeline operation safety monitoring and pipeline integrity maintenance. However, the diversity of cracks in the weld joints of pipelines leads to the complexity of time-varying acoustic emission signals, which limits the monitoring accuracy in practical applications. Therefore, a pipeline weld crack leakage monitoring system based on acoustic emission data image coding and deep learning model is proposed in this paper. Specifically, firstly, based on Markov transition field, the leakage signal collected by the AE monitoring system is encoded into two-dimensional image data, and the multi-dimensional phase space trajectory of the signal is revealed while strengthening the correlation and time dependence between the time series sampling points. Then, a residual Swin transformer network model is constructed to obtain useful information from AE coding images and identify different leakage conditions. Finally, experiments with different leakage states are designed to verify the superiority of the proposed method in multiple evaluation indexes, and the recognition accuracy rate reaches 97.86%. The comparison experiment with other methods further proves that the proposed monitoring strategy can be deployed online to maintain the safety of weld pipeline operation.
Similar content being viewed by others
Data availability
The data that has been used is confidential.
References
Morks MF (2008) Overview of recent welding technology relating to pipeline construction. Trans JWRI 37:1–5
Dubov A, Dubov A, Kolokolnikov S (2018) Detection of local stress concentration zones in engineering products—The lacking link in the non-destructive testing system. Welding in the world 62:301–309
Huang J, Zhang Z, Qin R, Yu Y, Wen G, Cheng W, Chen X (2023) Lightweight neural network architecture for pipeline weld crack leakage monitoring using acoustic emission. IEEE Trans Instrum Meas 72:1–10
Wang L, Mao Z, Xuan H, Ma T, Hu C, Chen J, You X (2022) Status diagnosis and feature tracing of the natural gas pipeline weld based on improved random forest model. Int J Press Vessels Pip 200:104821
Dakhel A, Gáspár M, Koncsik Z, Lukács J (2023) Fatigue and burst tests of full-scale girth welded pipeline sections for safe operations. Welding in the World 67:1193–1208
Abdullahi M (2019) Detection of leakage and blockage in pipeline systems. The University of Manchester (United Kingdom)
Xi G, Tan F, Yan L, Huang C, Shang T (2016) Design of an oil pipeline nondestructive examination system based on ultrasonic testing and magnetic flux leakage. Revista de la Facultad de Ingeniería 31:132–140
Dubov A, Kolokolnikov S (2013) The metal magnetic memory method application for online monitoring of damage development in steel pipes and welded joints specimens. Weld World 57:123–136
Zhang Z, Qin R, Li G, Du Z, Wen G, He W (2022) A novel approach for surface integrity monitoring in high-energy nanosecond-pulse laser shock peening: acoustic emission and hybrid-attention CNN. IEEE Trans Industr Inf 19:2802–2813
Ozevin D, Harding J (2012) Novel leak localization in pressurized pipeline networks using acoustic emission and geometric connectivity. Int J Press Vessels Pip 92:63–69
Zhang Z, Qin R, Yuan Y, Ren W, Yang Z, Wen G (2021) Acoustic emission-based weld crack in-situ detection and location using WT-TDOA. Trans Intelli Weld Manuf III(4 2019) Springer, pp 49-73
Van Hieu B, Choi S, Kim YU, Park Y, Jeong T (2011) Wireless transmission of acoustic emission signals for real-time monitoring of leakage in underground pipes. KSCE J Civ Eng 15:805–812
Quy TB, Kim J-M (2020) Leak localization in industrial-fluid pipelines based on acoustic emission burst monitoring. Measurement 151:107150
Huang J, Zhang Z, Zheng B, Qin R, Wen G, Cheng W, Chen X (2023) Acoustic emission technology-based multifractal and unsupervised clustering on crack damage monitoring for low-carbon steel. Measurement 217:113042
Zhang YM, Yang Y-P, Zhang W, Na S-J (2020) Advanced welding manufacturing: a brief analysis and review of challenges and solutions. J Manuf Sci Eng 142:110816
Sourav A, Gowtam D, Murthy J, Thangaraju S (2023) A study of microstructural evolution in gas tungsten arc welded AlxCoCrFeNi high entropy alloys. Weld World 67:2163–2174
Banjara NK, Sasmal S, Voggu S (2020) Machine learning supported acoustic emission technique for leakage detection in pipelines. Int J Press Vessels Pip 188:104243
Sun J, Xiao Q, Wen J, Zhang Y (2016) Natural gas pipeline leak aperture identification and location based on local mean decomposition analysis. Measurement 79:147–157
Ullah N, Ahmed Z, Kim J-M (2023) Pipeline leakage detection using acoustic emission and machine learning algorithms. Sensors 23:3226
Zhang Z, Huang Y, Qin R, Lei Z, Wen G (2021) Real-time measurement of seam strength using optical spectroscopy for Al–Li alloy in laser beam welding. IEEE Trans Instrum Meas 70:1–10
Carpinteri A, Lacidogna G, Pugno N (2007) Structural damage diagnosis and life-time assessment by acoustic emission monitoring. Eng Fract Mech 74:273–289
Zhang Y (2008) Real-time weld process monitoring. Elsevier
Qin R, Zhang Z, Huang J, Wang J, Du Z, Wen G, He W (2023) Surface stress monitoring of laser shock peening using AE time-scale texture image and multi-scale blueprint separable convolutional networks with attention mechanism. Expert Syst Appl 224:120018
Song Y, Li S (2021) Gas leak detection in galvanised steel pipe with internal flow noise using convolutional neural network. Process Saf Environ Prot 146:736–744
Zhang Z, Xu C, Xie J, Zhang Y, Liu P, Liu Z (2023) MFCC-LSTM framework for leak detection and leak size identification in gas-liquid two-phase flow pipelines based on acoustic emission. Measurement 219:113238
Han K, Xiao A, Wu E, Guo J, Xu C, Wang Y (2021) Transformer in transformer. Adv Neural Inf Process Syst 34:15908–15919
Marwan N, Romano MC, Thiel M, Kurths J (2007) Recurrence plots for the analysis of complex systems. Phys Rep 438:237–329
Wang Z, Oates T (2015) Imaging time-series to improve classification and imputation, arXiv preprint arXiv:1506.00327
Lee H, Yang K, Kim N, Ahn CR (2020) Detecting excessive load-carrying tasks using a deep learning network with a Gramian angular field. Autom Constr 120:103390
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF international conference on computer vision, pp. 10012–10022.
Liang J, Cao J, Sun G, Zhang K, Van Gool L, Timofte R (2021) Swinir: image restoration using swin transformer. Proceedings of the IEEE/CVF international conference on computer vision, pp 1833–1844
Li Z, Zhang H, Tan D, Chen X, Lei H (2017) A novel acoustic emission detection module for leakage recognition in a gas pipeline valve. Process Saf Environ Prot 105:32–40
Li S, Song Y, Zhou G (2018) Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition. Measurement 115:39–44
Zhu S-B, Li Z-L, Li X, Xu H-H, Wang X-M (2021) Convolutional neural networks-based valve internal leakage recognition model. Measurement 178:109395
Liu S, Mei J, Wang X, Zhu M, Gao J, Li Q, Cao Y (2023) Gas leak detection system in compressor stations based on a microphone array and multi-channel frequency Transformer. Measurement 219:113256
Chen Z, Cen J, Xiong J (2020) Rolling bearing fault diagnosis using time-frequency analysis and deep transfer convolutional neural network. IEEE Access 8:150248–150261
Acknowledgements
The authors acknowledge the research team members for their guidance and help.
Funding
This study is financially supported by the 14th Five-Year Equipment Preliminary Research Special Technology Project (No.3210405103).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Recommended for publication by Commission XV - Design, Analysis, and Fabrication of Welded Structures.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Huang, J., Zhang, Z., Qin, R. et al. Residual Swin transformer-based weld crack leakage monitoring of pressure pipeline. Weld World 68, 879–891 (2024). https://doi.org/10.1007/s40194-023-01632-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40194-023-01632-1