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p-DLA: A Predictive System Model for Onshore Oil and Gas Pipeline Dataset Classification and Monitoring - Part 1
Computer Vision and Pattern Recognition · Submitted 31 Dec 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim It also shows that the proposed new approach is competitive with a well-known and proven AI approach such as the LSTM.
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The abstract, sentence by sentence
With the rise in militant activity and rogue behaviour in oil and gas regions around the world, oil pipeline disturbances is on the increase leading to huge losses to multinational operators and the countries where such facilities exist. However, this situation can be averted if adequate predictive monitoring schemes are put in place. We propose in the first part of this paper, an artificial intelligence predictive monitoring system capable of predictive classification and pattern recognition of pipeline datasets. The predictive system is based on a highly sparse predictive Deviant Learning Algorithm (p-DLA) designed to synthesize a sequence of memory predictive clusters for eventual monitoring, control and decision making. The DLA (p-DLA) is compared with a popular machine learning algorithm, the Long Short-Term Memory (LSTM) which is based on a temporal version of the standard feed-forward back-propagation trained artificial neural networks (ANNs). The results of simulations study show impressive results and validates the sparse memory predictive approach which favours the sub-synthesis of a highly compressed and low dimensional knowledge discovery and information prediction scheme. It also shows that the proposed new approach is competitive with a well-known and proven AI approach such as the LSTM.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | With the rise in militant activity and rogue behaviour in oil and gas regions around the world, oil pipeline disturbances is on the increase leading to huge losses to multinational operators and the countries where such facilities exist. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | However, this situation can be averted if adequate predictive monitoring schemes are put in place. | background 0.78 | 0.04 | 0.05 | hedged possibility 0.67 |
| s2 | We propose in the first part of this paper, an artificial intelligence predictive monitoring system capable of predictive classification and pattern recognition of pipeline datasets. | contribution 1.00 | 0.85 | 0.09 | asserted 0.97 |
| s3 | The predictive system is based on a highly sparse predictive Deviant Learning Algorithm (p-DLA) designed to synthesize a sequence of memory predictive clusters for eventual monitoring, control and decision making. | method 0.96 | 0.73 | 0.06 | asserted 1.00 |
| s4 | The DLA (p-DLA) is compared with a popular machine learning algorithm, the Long Short-Term Memory (LSTM) which is based on a temporal version of the standard feed-forward back-propagation trained artificial neural networks (ANNs). | method 0.93 | 0.64 | 0.04 | asserted 0.99 |
| s5 | The results of simulations study show impressive results and validates the sparse memory predictive approach which favours the sub-synthesis of a highly compressed and low dimensional knowledge discovery and information prediction scheme. | result 0.82 | 0.93 | 0.47 | asserted 1.00 |
| s6 | It also shows that the proposed new approach is competitive with a well-known and proven AI approach such as the LSTM. | result 0.64 | 0.84 | 0.45 | asserted 0.99 |
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Original abstract
With the rise in militant activity and rogue behaviour in oil and gas regions around the world, oil pipeline disturbances is on the increase leading to huge losses to multinational operators and the countries where such facilities exist. However, this situation can be averted if adequate predictive monitoring schemes are put in place. We propose in the first part of this paper, an artificial intelligence predictive monitoring system capable of predictive classification and pattern recognition of pipeline datasets. The predictive system is based on a highly sparse predictive Deviant Learning Algorithm (p-DLA) designed to synthesize a sequence of memory predictive clusters for eventual monitoring, control and decision making. The DLA (p-DLA) is compared with a popular machine learning algorithm, the Long Short-Term Memory (LSTM) which is based on a temporal version of the standard feed-forward back-propagation trained artificial neural networks (ANNs). The results of simulations study show impressive results and validates the sparse memory predictive approach which favours the sub-synthesis of a highly compressed and low dimensional knowledge discovery and information prediction scheme. It also shows that the proposed new approach is competitive with a well-known and proven AI approach such as the LSTM.
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