REVIEW: EFEK EMISI AKUSTIK UNTUK MENDETEKSI KERUSAKAN DALAM PIPA
Keywords:
Acoustic Emission, Pipeline, Pipe DamageAbstract
Pipelines are vital elements in the oil and gas infrastructure, and damage to them can have serious consequences. This article aims to determine or study the basic concepts of acoustic emissions, damage detection principles, and their practical applications in industries that are closely related to pipeline systems. The methodology used in this article is a literature review. The result is that the acoustic emission method has advantages including its ability to detect damage at an accurate early stage, minimize downtime, and reduce maintenance costs. However, acoustic emission has several disadvantages such as, accuracy is greatly affected by environmental changes and external signal interference. The sensors used in acoustic emission are also very expensive. Overall, acoustic emission is a very effective method to detect damage in pipelines, the weaknesses contained in it are a challenge for future researchers.References
Adegboye, M. A., Fung, W. K., & Karnik, A. (2019). Recent advances in pipeline monitoring and oil leakage detection technologies: Principles and approaches. Dalam Sensors (Switzerland) (Vol. 19, Nomor 11). MDPI AG.
Ahmad, Z., Nguyen, T. K., & Kim, J. M. (2023). Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network. Engineering Applications of Computational Fluid Mechanics, 17(1).
Alamri, A. H. (2020). Localized corrosion and mitigation approach of steel materials used in oil and gas pipelines – An overview. Dalam Engineering Failure Analysis (Vol. 116). Elsevier Ltd.
Bui Quy, T., & Kim, J. M. (2020). Leak detection in a gas pipeline using spectral portrait of acoustic emission signals. Measurement: Journal of the International Measurement Confederation, 152.
Ceravolo, R., Civera, M., Lenticchia, E., Miraglia, G., & Surace, C. (2021). Damage Detection and Localisation in Buried Pipelines using Entropy in Information Theory. 7830.
Duong, B. P., Kim, J., Kim, C. H., & Kim, J. M. (2019). Deep learning object-impulse detection for enhancing leakage detection of a boiler tube using acoustic emission signal. Applied Sciences (Switzerland), 9(20).
Duong, B. P., & Kim, J. M. (2018). Pipeline fault diagnosis using wavelet entropy and ensemble deep neural technique. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10884 LNCS, 292–300.
Duong, B. P., Kim, J. Y., Jeong, I., Kim, C. H., & Kim, J. M. (2020). Acoustic emission burst extraction for multi-level leakage detection in a pipeline. Applied Sciences (Switzerland), 10(6), 1–11.
Gao, Y., Piltan, F., & Kim, J. M. (2022). A Hybrid Leak Localization Approach Using Acoustic Emission for Industrial Pipelines. Sensors, 22(10).
Ju, T., & Findikoglu, A. T. (2021). Monitoring of Corrosion Effects in Pipes with Multi-Mode Acoustic Signals.
Lu, H., Iseley, T., Behbahani, S., & Fu, L. (2020). Leakage detection techniques for oil and gas pipelines: State-of-the-art. Dalam Tunnelling and Underground Space Technology (Vol. 98). Elsevier Ltd.
Lukonge, A. B., & Cao, X. (2020). Leak Detection System for Long-Distance Onshore and Offshore Gas Pipeline Using Acoustic Emission Technology. A Review. Dalam Transactions of the Indian Institute of Metals (Vol. 73, Nomor 7, hlm. 1715–1727). Springer.
Nohal, L., Mazal, P., Vlasic, F., & Svobodova, M. (2019). Acoustic emission response to erosion-corrosion and creep damage in pipeline systems. Procedia Structural Integrity, 23, 227–232.
Okudan, G., Danawe, H., Zhang, L., Ozevin, D., & Tol, S. (2021). Enhancing acoustic emission characteristics in pipe-like structures with gradient-index phononic crystal lens. Materials, 14(6).
Onyebuchi, V. E., Kolios, A., Hanak, D. P., Biliyok, C., & Manovic, V. (2018). A systematic review of key challenges of CO2 transport via pipelines. Dalam Renewable and Sustainable Energy Reviews (Vol. 81, hlm. 2563–2583). Elsevier Ltd.
Peng, Z., Jian, J., Wen, H., Gribok, A., Wang, M., Liu, H., Huang, S., Mao, Z.-H., & Chen, K. P. (2020). Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions. Optics Express, 28(19), 27277.
Quy, T. B., & Kim, J. M. (2020). Leak localization in industrial-fluid pipelines based on acoustic emission burst monitoring. Measurement: Journal of the International Measurement Confederation, 151.
Quy, T. B., & Kim, J. M. (2021). Crack detection and localization in a fluid pipeline based on acoustic emission signals. Mechanical Systems and Signal Processing, 150.
Saboor, A., Rimsan, M., Hassan, F., Kamil Mahmood, A., Ab Rahim, L., & Muslim Jameel, S. (2021). Clustering-Based Quantitative Evaluation Using Acoustic Emission Waveforms for Corrosion Detection (Vol. 48, Nomor 7).
Ullah, N., Ahmed, Z., & Kim, J. M. (2023). Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms. Sensors, 23(6).
Zhao, H., Li, Z., Zhu, S., & Yu, Y. (2020). Valve internal leakage rate quantification based on factor analysis and wavelet-bp neural network using acoustic emission. Applied Sciences (Switzerland), 10(16).
Indonesian Society of Applied Science (ISAS)
