[PDF] Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM | Semantic Scholar (2024)

A pattern recognition method that first extracts time-domain and frequency-domain features from vibration signals to represent each fault distinctly, and these features are then utilized with a classifier, i.e. support vector machine (SVM), to classify fault types.

The source of oil spill is detected through swarm robots along with the use of swarm intelligence algorithm i.e. Modified Glowworm Swarm optimization(MGSO) Algorithm to speed up the convergence rate.

Experimental results show that least squares support vector machine optimized by swarm intelligence techniques can effectively handle classification task on different datasets especially on those datasets with limited samples and mixed attributes.

An extreme learning machine (ELM) method is proposed to detect the pipeline leakage online and the simulation results showed that the performance of ELM is better than BP and RBF neural networks on the Pipeline leakage classification accuracy and speed.

This research aims to study the ability of data-driven intelligent models to detect small leaks for a natural gas pipeline using basic operational parameters and then compare the intelligent models among themselves using existing performance metrics.

A novel data-based leakage diagnosis method for big datasets, which identifies the leak occurrence, its size, and its location, and significantly outperforms others with the average correct classification rate (CCR) of about 98%.

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Experimental results demonstrate that, when compared to the Wavelet based methods, the proposed SVM framework offers the better performance.

A fuzzy decision-making approach to oil pipeline leak localization is proposed, where the two main methods, pressure gradient localization and negative pressure wave localization are combined with fuzzy logical decision- making to form a novel fault diagnosis scheme.

A leak detection system for pipelines was developed by using artificial neural networks for leak sizing and location and by processing the field data by using a computer code in conjunction with the ANN to compensate for the operational variations and to prevent spurious alarms.

A support vector machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters was presented and it was compared to a classification elaborated by a Petroleum Engineering expert.

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