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Reseach Article

Oil and Gas Pipeline Monitoring using Artificial Neural Network

by Nuka D. Nwiabu, Kingsley E. Igbudu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 1
Year of Publication: 2017
Authors: Nuka D. Nwiabu, Kingsley E. Igbudu

Nuka D. Nwiabu, Kingsley E. Igbudu . Oil and Gas Pipeline Monitoring using Artificial Neural Network. International Journal of Computer Applications. 180, 1 ( Dec 2017), 12-18. DOI=10.5120/ijca2017915893

@article{ 10.5120/ijca2017915893,
author = { Nuka D. Nwiabu, Kingsley E. Igbudu },
title = { Oil and Gas Pipeline Monitoring using Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 180 },
number = { 1 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017915893 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:59:25.148176+05:30
%A Nuka D. Nwiabu
%A Kingsley E. Igbudu
%T Oil and Gas Pipeline Monitoring using Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 1
%P 12-18
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Conventional Artificial Neural Network approaches such as Feed-Forward Networks has been used in diverse applications but are not naturally predictive and also require supervised learning. Feed-forward Artificial Neural Network also trained by backpropagation poses the problem of varnishing gradient. Long Short Term Memory is an Artificial Neural Network recurrent technique that allows long range contextual representation to be stored and learnt in an unsupervised manner. In this work a modified Sparse Distributed LSTM Algorithm using Gaussian membership function with a context-decision gate for detection and monitoring operations has been proposed as an alternative to the traditional Feed-Forward Architecture. The AI monitoring System shows promising results in solving many recurrent problems, particularly those requiring long-term storage dependencies - the Vanishing Gradient problem (VGP) and has the ability to use contextual information when mapping between input and output sequences. The Oil and Gas AI monitoring system employs dynamic data flow modeling to simulate the behavior of probably militant behaviors. The contextual information (context data) includes such context as Pressure; Vehicle passing along the pipeline area, Manual digging, and Machine excavation. Dynamic simulations were performed using a real-time data obtained from the SPDC. The data is tested using AI system in MATLAB-SIMULINK environment to verify the performance of the proposed system. The results were promising indicating the real state of vandalism prediction.

  1. Monica, A. and Collopy, F. 1998. Journal of Forecasting 17, 481- 495
  2. Krenker, A., Janez, B. and Kos, A. 2011. Introduction to the Artificial Neural Network - Methodological advances and Biomedical Application ISBN: 978-953-307-243-2
  3. Hochreiter, S., and Schmidhuber, J. 1997. Long short-term memory. Neural computation, 9(8), 1735-1780.
  4. Ntalampiras, S., Soupionis, Y.,and Giannopoulos, G. 2015.A fault diagnosis system for interdependentcritical infrastructures based on HMMs.Reliability Engineering& System Safety, 138, 73-81.
  5. de Ruijter, A., and Guldenmund, F. 2016. The bowtie method: A review. Safety Science
  6. Esher, L., Hall, S., Regnier, E., Sánchez, P. J., Hansen, J. A., and Singham, D. 2010. Simulating pirate behavior to exploit environmental information. In Simulation Conference (WSC), Proceedings of the 2010 winter pp. 1330-1335. IEEE.
  7. Bouejla, A., Chaze, X., Guarnieri, F., and Napoli, A. 2014. A Bayesian network to manage risks of maritime piracy against offshore oil fields. Safety Science, 68, 222-230.
  8. Gómez-Romero, J., Serrano, M. A., García, J., Molina, J. M., and Rogova, G. 2015. Context-based multi-level information fusion for harbor surveillance. Information Fusion, 21, 173-186.
  9. Nwiabu, N.D. 2012. Situation Awareness Approach to Context-Aware Case-Based Decision Support, A thesis submitted in partial fulfillment of the requirements of Robert Gordon University for the degree of Doctor of Philosophy
  10. Jacobson, I., Grady, B., Rumbaugh, J. and Addison-Wesley. 1999 Unified Software Development Process,
  11. Grady B. and Addison-Wesley. 1995. Object Solutions
  12. P. Kruchten, R. Capilla JC Duenas – The Decision view’s role in software architecture practice
  13. Suzuki, K. 2011. Artificial Neural Networks- Methodological Advances and Biomedical Applications
  14. Negnevitsky, M. 2002. Artificial Intelligence: A Guide to Intelligent Systems 3rd Edition.
Index Terms

Computer Science
Information Sciences


Artificial neural network Sparse Distributed LSTM vandalism Oil and Gas pipeline monitoring Recurrent Neural Network.