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Pipeline Leakage Detection and Characterisation with Adaptive Surrogate Modelling using Particle Swarm Optimisation

conference contribution
posted on 2023-10-10, 15:48 authored by Mutiu Adesina Adegboye, Aditya Karnik, Wai Keung FungWai Keung Fung, Radhakrishna Prabhu

Pipelines are often subject to leakage due to ageing, corrosion, and weld defects, and it is difficult to avoid as the sources of leakages are diverse. Several studies have demonstrated the applicability of the machine learning model for the timely prediction of pipeline leakage. However, most of these studies rely on a large training data set for training accurate models. The cost of collecting experimental data for model training is huge, while simulation data is computationally expensive and time-consuming. To tackle this problem, the present study proposes a novel data sampling optimisation method, named adaptive particle swarm optimisation (PSO) assisted surrogate model, which was used to train the machine learning models with a limited dataset and achieved good accuracy. The proposed model incorporates the population density of training data samples and model prediction fitness to determine new data samples for improved model fitting accuracy. The proposed method is applied to 3-D pipeline leakage detection and characterisation. The result shows that the predicted leak sizes and location match the actual leakage. The significance of this study is two-fold: the practical application allows for pipeline leak prediction with limited training samples and provides a general framework for computational efficiency improvement using adaptive surrogate modelling in various real-life applications.

Funding

Petroleum Technology Development Fund (PTDF), Abuja Nigeria, funded this research. Grant number PTDF/ED/PHD/AMM/1385/18

History

Presented at

The 2022 9th International Conference on Soft Computing & Machine Intelligence, ISCMI 2022

Link to Conference Website

Publisher

IEEE

Publication Year

2022

Version

  • AM (Accepted Manuscript)

Citation

M. A. Adegboye, A. Karnik, W. -K. Fung and R. Prabhu, "Pipeline Leakage Detection and Characterisation with Adaptive Surrogate Modelling Using Particle Swarm Optimisation," 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), Toronto, ON, Canada, 2022, pp. 129-134, doi: 10.1109/ISCMI56532.2022.10068436.

Electronic ISSN

2640-0146

ISBN

979-8-3503-2088-6

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Wai Keung Fung

Cardiff Met Research Centre/Group

EUREKA Robotics Centre

Copyright Holder

  • © The Publisher

Publisher Rights Statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Language

  • en

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