TY - GEN
T1 - Pipeline leak detection using artificial neural network
T2 - 2013 5th International Conference on Modelling, Identification and Control, ICMIC 2013
AU - Abdulla, Mohammad Burhan
AU - Herzallah, Randa Omar
AU - Hammad, Mahmoud Ahmad
PY - 2013/10/24
Y1 - 2013/10/24
N2 - Pipeline transportation of resources is considered a vital method due to low operational cost, and simple design and implementation. However, potential leaks compromise the integrity of this method. Pipeline leaks consequences are major concerns due to resources loss, environmental impact and potential human injuries and fatalities. This paper investigates neural network based probabilistic decision support system for detecting the presence of leak in pipeline transportation systems. The probabilistic model correlates measurements of inlet and outlet pressures and flow to leak status. Several pipeline leak detection methods have been developed, nevertheless, noisy data, and changes in operational conditions are the main challenges that limit the performance of leak detection leading to high false alarms. ANN properties of noise-immunity characteristics, parallel structure and correspondingly fast processing and classification capabilities provide enhanced performance of leak detection.
AB - Pipeline transportation of resources is considered a vital method due to low operational cost, and simple design and implementation. However, potential leaks compromise the integrity of this method. Pipeline leaks consequences are major concerns due to resources loss, environmental impact and potential human injuries and fatalities. This paper investigates neural network based probabilistic decision support system for detecting the presence of leak in pipeline transportation systems. The probabilistic model correlates measurements of inlet and outlet pressures and flow to leak status. Several pipeline leak detection methods have been developed, nevertheless, noisy data, and changes in operational conditions are the main challenges that limit the performance of leak detection leading to high false alarms. ANN properties of noise-immunity characteristics, parallel structure and correspondingly fast processing and classification capabilities provide enhanced performance of leak detection.
KW - artificial neural networks
KW - negative pressure wave
KW - Pipeline leak detection
KW - stochastic noise
KW - uncertainty
UR - http://www.scopus.com/inward/record.url?scp=84890882307&partnerID=8YFLogxK
UR - https://ieeexplore.ieee.org/document/6642219
M3 - Conference publication
AN - SCOPUS:84890882307
SN - 9780956715739
T3 - 2013 Proceedings of International Conference on Modelling, Identification and Control, ICMIC 2013
SP - 328
EP - 332
BT - 2013 Proceedings of International Conference on Modelling, Identification and Control, ICMIC 2013
PB - IEEE
Y2 - 31 August 2013 through 2 September 2013
ER -