Real-time Monitoring Of Gas Pipeline Through Artificial Neural Networks

Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on acoustic method and on-line prediction of leak location using neural artificial networks. Audible noises generated by leakage were captured by a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1kHz, 5kHz and 9kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence, magnitude and location of a leak (outputs of the model). The results have shown the great potential of the technique and of the developed neural models. For all on-line tests, the neural model 1 (responsible for determining the occurrence and magnitude of the leak) showed 100% accuracy, except when the leakage occurred through a small orifice (1 mm), with leak located at 3 m from the microphone. In all cases where neural model 1 detected the leak, the neural model 2 (responsible determining the location) could accurately predict the exact location of the leak, except for an orifice of 3 mm, with leakage occurring at the inlet end of the pipeline, showing an error of approximately 1.2 m. © 2013 IEEE.329334Kim, M.S., Lee, S.K., Detection of leak acoustic signal in buried gas pipe based on the time-frequency analysis (2009) Journal of Loss Prevention in the Process Industries, 22, pp. 990-994Zhang, S., Asakura, T., Hayashi, S., Gas leakage fault Detection of pneumatic pipe system using" (2004) JSME International Journal, 47 (2), pp. 568-573Taghvaei, M., Beck, S.B.M., Staszewski, W.J., Leak detection in pipeline networks using low-profile piezoceramic transducers (2007) Structural Control & Health Monitoring, 14, pp. 1063-1082Tolstoy, A., Horoshenkov, K.V., Bin Ali, M.T., Detection pipe changes via acoustic matched field processing (2009) Journal Applied Acoustics, 70, pp. 695-702Yang, J., Wen, Y., Li., P., Leak acoustic detection in water distribution pipelines (2008) Proceedings of 7th World Congress on Intelligent Control and Automation, pp. 3057-3061Sousa, E.O., Cruz, S.L., Pereira, J.A.F.R., Detecção de vazamentos em tubulações". (2008) Transientes Hidráulicos e Acústicos, Revista Controle & Instrumentação, 135, pp. 68-72Avelino, A.M., De Paiva, J.A., Da Silva, R.E.F., Araujo, G.J.M., Real time leak detection system applied to oil pipelines using sonic technology and neural networks (2009) 35th Annual Conference of IEEE, pp. 2109-2114Caixia, C., Fuchun, S., Xinquan, Z., One fire detection method using neural networks Thinghua Science Technology, 16 (1), p. 2011Belsito, S., Lombardi, P., Andreussi, P., Banerjee, S., Leak detection in liquefied gas pipelines by artificial networks (1998) AIChE J., 44 (12), pp. 2675-2688Garcia, F.M., Quadri, R.A., Machado, R.A.F., Bolzan, A., Analysis of time / frequency for vibroacoustic detection of leaks of petroleum and gas industry" (in portuguese (2010) Brazilian Congress of Chemical Engineering, pp. 5745-5753Shibata, A., Konichi, M., Abe, Y., Hasegawa, R., Watanable, M., Kamijo, H., Neuro based classification of gas leakage sounds in pipeline (2009) Proceedings of International Conference on Networking, Sensing and Control, pp. 298-302Zhao, J., Li, D., Qi, H., Sun, F., An., R., The fault diagnosis method of pipeline leakage based on neural network (2010) Proceedings of International Conference on Computer, Mechatronics, Control and Electronic Engineering, pp. 322-325Pavan, M., Online leak detection in pressure vessels using sonic systems" (in portuguese) (2005) School of Chemical Engineering, , University of Campinas Thesis (Master