ARCHIVES

1. Covas, D., Ramos, H., & Almeida, A. (2005). Standing wave difference method for leak detection in pipeline systems. Journal of Hydraulic
Engineering, 131(12), 1106-1116.
2. Li, Y., Zhou, Y., Fu, M., Zhang, F., Chi, Z., & Wang, W. (2021). Analysis of propagation and distribution characteristics of leakage
acoustic waves in water supply pipelines. Sensors, 21(16), 5450.
3. Mysorewala, M., Sabih, M., Cheded, L., Nasir, M., & Ismail, M. (2015). A novel energy-aware approach for locating leaks in water
pipeline using a wireless
4. Fouad, M., Al-AWADI, H., Moustafa, F., & Nawara, M. (2008). Leak detection and localization of the pipeline systems. The International
Conference on Applied Mechanics and Mechanical Engineering, 13(13), 431-448.
5. Sun, J., Liu, Z., Wen, J., & Qiao, Y. (2022). Leakage aperture identification of natural gas pipeline based on compressed acquisition and
dsae. Structural Health Monitoring, 22(4), 2856-2867.
6. Jia, Z., Ren, L., Li, H., Jiang, T., & Wu, W. (2018). Pipeline leakage identification and localization based on the fiber bragg grating hoop
strain measurements and particle swarm optimization and support vector machine. Structural Control and Health Monitoring, 26(2),
e2290.
7. Bohorquez, J., Lambert, M., Alexander, B., Simpson, A., & Abbott, D. (2022). Stochastic resonance enhancement for leak detection in
pipelines using fluid transients and convolutional neural networks. Journal of Water Resources Planning and Management, 148(3).
8. Yan, W., Liu, W., Bi, H., Jiang, C., Yang, D., Sun, S. … & Sun, Y. (2023). Acoustic detection and localization of gas pipeline leak
based on residual connection and one-dimensional separable convolutional neural network. Transactions of the Institute of Measurement
and Control, 45(14), 2637-2647.
9. Hai-ou, S., Zhu, Y., & Lang, X. (2022). A small leak detection and localization method for oil pipelines based on improved robust principle
component analysis. Journal of Physics Conference Series, 2264(1), 012021.
10. Yan, W., Liu, W., Bi, H., Jiang, C., Yang, D., Sun, S., & Sun, Y. (2023). Acoustic detection and localization of gas pipeline leak based on
residual connection and one-dimensional separable convolutional neural network. Transactions of the Institute of Measurement and
Control, 45(14), 2637-2647.
11. Civan, F. and Balda, K. (2013). Application of mass balance and transient flow modeling for leak detection in liquid pipelines.
12. Zhou, M., Zhang, Q., Liu, Y., Sun, X., Cai, Y., & Pan, H. (2019). An integration method using kernel principal component analysis and
cascade support vector data description for pipeline leak detection with multiple operating modes