Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines

doi:10.1016/j.aej.2023.10.026

淡江大學機構典藏 > 工學院 > 人工智慧學系 > 期刊論文 > Item 987654321/125176

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/125176

Title:	Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines
Authors:	Chen, Tzu-Chia;Almimi, Hani;Daoud, Mohammad Sh.;Guerrero, John William Grimaldo;Chorzępa, Rafał
Keywords:	Feature extraction;Feature selection technique;Group method of Data Handling (GMDH) neural network;Particle Swarm Optimization;X-ray tube-based system
Date:	2023-11
Issue Date:	2024-03-07 12:06:21 (UTC+8)
Publisher:	Alexandria University * Faculty of Engineering
Abstract:	Pipeline installation is a time-consuming and expensive process in the oil sector. Because of this, a pipe is often utilized to carry diverse petroleum products; hence, it is crucial to use a precise and dependable control system to identify the kind and quantity of oil products being transported. This study attempts to identify four petroleum products by using an X-ray tube-based system, feature extraction in the frequency and temporal domains, and feature selection using Particle Swarm Optimization (PSO) in conjunction with a Group Method of Data Handling (GMDH) neural network. A sodium iodide detector, a test pipe that simulates petroleum compounds, and an X-ray source make up the implemented system. The detector's output signals were transmitted to the frequency domain, where the amplitudes of the top five dominant frequencies could be determined. Furthermore, the received signals were analyzed to extract five temporal characteristics-MSR, 4th order moment, skewness, WL, and kurtosis. The PSO system takes into account the extracted time and frequency features as input in order to introduce the optimal combination. Four different GMDH neural networks were constructed, and the chosen characteristics were used as inputs for those networks. Finding the volume ratio of each product was the responsibility of each neural network. The four designed neural networks were able to predict the amount of ethylene glycol, crude oil, gasoil, and gasoline with RMSE of 0.26, 0.17, 0.19, and 0.23, respectively. One compelling argument for using the proposed approach in the oil industry is that it can calculate the volume ratio of products with a root mean square error of no more than 0.26. The adoption of a feature selection method to choose the best ones is credited with this remarkable degree of precision. By providing appropriate inputs to neural networks, the control system has significantly outperformed its predecessors in terms of precision and efficiency.
Relation:	Alexandria Engineering Journal 82, p.518-530
DOI:	10.1016/j.aej.2023.10.026
Appears in Collections:	[人工智慧學系] 期刊論文

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