[
Aljaroudi, A., Khan, F., Akinturk, A., Haddara, M., Thodi, P., 2015. Risk assessment of offshore crude oil pipeline failure. Journal of Loss Prevention in the Process Industries, 37, 101–109. DOI: 10.1016/j.jlp.2015.07.004
]Search in Google Scholar
[
Alzyod, H., Ficzere, P., 2023. Correlation Between Printing Parameters and Residual Stress in Additive Manufacturing: A Numerical Simulation Approach. Production Engineering Archives, 29, 279–287.
]Search in Google Scholar
[
Amyotte, P.R., Berger, S., Edwards, D.W., Gupta, J.P., Hendershot, D.C., Khan, F.I., Mannan, M.S., Willey, R.J., 2016. Why major accidents are still occurring. Current Opinion in Chemical Engineering. Biotechnology and bioprocess engineering / Process systems engineering, 14, 1–8. DOI: 10.1016/j.coche.2016.07.003
]Search in Google Scholar
[
ASTM E8 / E8M-16ae1, 2016. ASTM E8 / E8M-16ae1, Standard Test Methods for Tension Testing of Metallic Materials. ASTM International, West Conshohocken.
]Search in Google Scholar
[
Avelino, Á.M., de Paiva, J.Á., da Silva, R.E.F., de Araujo, G.J.M., de Azevedo, F.M., de O. Quintaes, F., Maitelli, A.L., Neto, A.D.D., Salazar, A.O., 2009. Real time leak detection system applied to oil pipelines using sonic technology and neural networks, in: 2009 35th Annual Conference of IEEE Industrial Electronics. Presented at the 2009 35th Annual Conference of IEEE Industrial Electronics, pp. 2109–2114. DOI: 10.1109/IECON.2009.5415324
]Search in Google Scholar
[
Benhamena, A., Fatima, B., Foudil, K., Baltach, A., Chaouch, M.I., 2023. Numerical Analysis of Fracture Behavior of Functionally Graded Materials using 3D-XFEM. Advances in Materials Science, 23, 33–46.
]Search in Google Scholar
[
Bokůvka, O., Jambor, M., Trško, L., Nový, F., Lisiecka, B., 2018. Fatigue lifetime of 20MnV6 steel with holes manufactured by various methods. Production Engineering Archives, 19, 3–5. DOI: 10.30657/pea. 2018.19.01
]Search in Google Scholar
[
Cataldo, A., Cannazza, G., De Benedetto, E., Giaquinto, N., 2012. A New Method for Detecting Leaks in Underground Water Pipelines. IEEE Sensors Journal, 12, 1660–1667. DOI: 10.1109/JSEN.2011.2176484
]Search in Google Scholar
[
Cui, X., Yan, Y., Ma, Y., Ma, L., Han, X., 2016. Localization of CO2 leakage from transportation pipelines through low frequency acoustic emission detection. Sensors and Actuators A: Physical, 237, 107–118. DOI: 10.1016/j.sna. 2015.11.029
]Search in Google Scholar
[
Kubicki, K., 2023. Technical and economic aspects of load-bearing welded joints in reinforcing steel. Construction of Optimized Energy Potential, 12(1), 228–235. DOI: 10.17512/bozpe.2023.12.25
]Search in Google Scholar
[
Feng, J., Li, F., Lu, S., Liu, J., Ma, D., 2017. Injurious or Noninjurious Defect Identification From MFL Images in Pipeline Inspection Using Convolutional Neural Network. IEEE Transactions on Instrumentation and Measurement, 66, 1883–1892. DOI: 10.1109/TIM.2017.2673024
]Search in Google Scholar
[
Gumen, O., Ujma, A., Kruzhkova, M., 2021. Research into the process of spraying complex titanium and zirconium nitride on structural steel and reaction times relating to the final finish and quality obtained. Construction of Optimized Energy Potential, 10, 71–76. DOI: 10.17512/bozpe.2021.1.07
]Search in Google Scholar
[
Hu, Z., Tariq, S., Zayed, T., 2021. A comprehensive review of acoustic based leak localization method in pressurized pipelines. Mechanical Systems and Signal Processing, 161, 107994. DOI: 10.1016/j.ymssp.2021.107994
]Search in Google Scholar
[
Jin, H., Zhang, L., Liang, W., Ding, Q., 2014. Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method. Journal of Loss Prevention in the Process Industries, 27, 74–88. DOI: 10.1016/j.jlp.2013.11.006
]Search in Google Scholar
[
Li, J., Zheng, Q., Qian, Z., Yang, X., 2019. A novel location algorithm for pipeline leakage based on the attenuation of negative pressure wave. Process Safety and Environmental Protection, 123, 309–316. DOI: 10.1016/j.psep. 2019.01.010
]Search in Google Scholar
[
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 Safety and Environmental Protection, 105, 32–40. DOI: 10.1016/j.psep.2016.10.005
]Search in Google Scholar
[
Liu, C., Li, Y., Meng, L., Wang, W., Zhang, F., 2014. Study on leak-acoustics generation mechanism for natural gas pipelines. Journal of Loss Prevention in the Process Industries, 32, 174–181. DOI: 10.1016/j.jlp.2014.08.010
]Search in Google Scholar
[
PN-EN ISO 6892-1:2020-05, 2019. PN-EN ISO 6892-1:2020-05, Metallic materials — Tensile testing — Part 1: Method of test at room temperature. International Organization for Standardization, Geneva.
]Search in Google Scholar
[
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. DOI: 10.1016/j.measurement.2015.10.015
]Search in Google Scholar
[
Świt, G., Dzioba, I., Adamczak-Bugno, A., Krampikowska, A., 2022. Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals. Materials, 15, 2659. DOI: 10.3390/ma15072659
]Search in Google Scholar
[
Świt, G., Dzioba, I., Ulewicz, M., Lipiec, S., Adamczak-Bugno, A., Krampikowska, A., 2023. Experimental-numerical analysis of the fracture process in smooth and notched V specimens. Production Engineering Archives, 29, 444–451. DOI: 10.30657/pea.2023.29.49
]Search in Google Scholar
[
Wang, F., Lin, W., Liu, Z., Wu, S., Qiu, X., 2017. Pipeline Leak Detection by Using Time-Domain Statistical Features. IEEE Sensors Journal, 17, 6431–6442. DOI: 10.1109/JSEN.2017.2740220
]Search in Google Scholar
[
Wang, L., Narasimman, S.C., Reddy Ravula, S., Ukil, A., 2017. Water Ingress Detection in Low-Pressure Gas Pipelines Using Distributed Temperature Sensing System. IEEE Sensors Journal, 17, 3165–3173. DOI: 10.1109/JSEN.2017.2686982
]Search in Google Scholar
[
Xiao, R., Hu, Q., Li, J., 2019. Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine. Measurement, 146, 479–489. DOI: 10.1016/j.measurement.2019.06.050
]Search in Google Scholar
[
Xu, Q., Zhang, L., Liang, W., 2013. Acoustic detection technology for gas pipeline leakage. Process Safety and Environmental Protection, 91, 253–261. DOI: 10.1016/j.psep.2012.05.012
]Search in Google Scholar
[
Zadkarami, M., Shahbazian, M., Salahshoor, K., 2017. Pipeline leak diagnosis based on wavelet and statistical features using Dempster–Shafer classifier fusion technique. Process Safety and Environmental Protection, 105, 156–163. DOI: 10.1016/j.psep.2016.11.002
]Search in Google Scholar