Fault Identification by Extreme Pressure in Oil Pipelines using Artificial Neural Network
E.B.Priyanka1, S.Thangavel2, P.Parameswari3, S.Ravi Sankar4, N. Selva Kumar5, R.Vignesh6
1E.B.Priyanka, Research Scholar, Department of Mechatronics Engineering, Kongu Engineering College, Perundurai, India.
2S.Thangavel, Faculty, Department of Mechatronics Engineering, Kongu Engineering College, Perundurai, India.
3P.Parameswari, Faculty, Department of Computer Appplication, Kumaraguru College of Technology, Coimbatore, India.
4S.Ravi Sankar, UG Scholar, Department of Mechatronics Engineering, Kongu Engineering College, Perundurai, India.
5N.Selava Kumar, UG Scholar, Department of Mechatronics Engineering, Kongu Engineering College, Perundurai, India.
6R.Vignesh, UG Scholar, Department of Mechatronics Engineering, Kongu Engineering College, Perundurai, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 90-96 | Volume-8 Issue-6, August 2019. | Retrieval Number: E7456068519/2019©BEIESP | DOI: 10.35940/ijeat.E7456.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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. Algorithm using Gaussian membership function with a context-decision gate for detection 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 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 from the lab scale experimental setup of oil pipeline system. In this approach, not only the networks are trained to adapt to the given training data, the training data (the expected outputs of fault indices) is also updated to adapt to the neural network. During the training procedure, both the neural networks and training data are updated interactively. Dynamic simulations were performed using a real-time data obtained from the Radial Bias Kernel Network. 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 fault identification in oil pipeline system caused by extreme pressure during transportation.
Keywords: Oil Pipelines, Pressure, Neural network.