Abstract
One of the main problems in the oil industrial field is the leakage in
transporting pipelines due to its effect on human society , environment, and money loss.
Therefore, the bottleneck for most researches in this subject is to minimize false alarm rate
(FAR) for the adopted leak detection method. Although some recent methods succeed in
classifying the existence or absence of the leak as a binary classification problem. But this
paper proposed a novel leak detection technique which predicts the leak location and
estimates its size within certain pre-defined ranges. In order to simulate the environmental
conditions for real-time operating oil pipeline, accurate simulator known as OLGA
program creates the oil physical parameters. Various methods for features extraction are
considered such as statistical and wavelet techniques which are implemented to get the
features from the fluid simulated waveforms. These features are organized and fed to an
ANN classifier trained by PSO algorithm to determine the leak class out of 10 suggested
classes. The proposed leak detection technique is used to simulate 18 kilometers belonging
to Iraqi crude oil pipelines company operated in Baghdad. The achieved results of the true
positive rate (TPR) for the proposed applied method for the leak detection and
classification of different leak classes in terms of their positions and magnitudes were
about 97%.
transporting pipelines due to its effect on human society , environment, and money loss.
Therefore, the bottleneck for most researches in this subject is to minimize false alarm rate
(FAR) for the adopted leak detection method. Although some recent methods succeed in
classifying the existence or absence of the leak as a binary classification problem. But this
paper proposed a novel leak detection technique which predicts the leak location and
estimates its size within certain pre-defined ranges. In order to simulate the environmental
conditions for real-time operating oil pipeline, accurate simulator known as OLGA
program creates the oil physical parameters. Various methods for features extraction are
considered such as statistical and wavelet techniques which are implemented to get the
features from the fluid simulated waveforms. These features are organized and fed to an
ANN classifier trained by PSO algorithm to determine the leak class out of 10 suggested
classes. The proposed leak detection technique is used to simulate 18 kilometers belonging
to Iraqi crude oil pipelines company operated in Baghdad. The achieved results of the true
positive rate (TPR) for the proposed applied method for the leak detection and
classification of different leak classes in terms of their positions and magnitudes were
about 97%.
Keywords
Artificial Neural Network (ANN)
Hybrid Classifier.
Leakage Detection
Particle Swarm Optimization (PSO)