Failure classification in natural gas pipe-lines using artificial intelligence: A case study
Gas pipelines are often subjected to various kinds of damages such as corrosion, welding failure, and excavation damages, due to harsh environmental conditions. The failure in gas pipelines may lead to catastrophic damages like human life loss, economic loss, etc. Predicting pipeline health is of cr...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1c6ea21235f3415a8c05cf8e0f9e457c |
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| Sumario: | Gas pipelines are often subjected to various kinds of damages such as corrosion, welding failure, and excavation damages, due to harsh environmental conditions. The failure in gas pipelines may lead to catastrophic damages like human life loss, economic loss, etc. Predicting pipeline health is of critical importance to avoid these damages. In this study, 875 incidents are extracted from US DOT PHMSA from 2002 to 2020. For each of the incident, different parameters such as Age, NPS, Wall Thickness, Material, Operating Pressure, Location, and Area is analyzed. Two supervised learning techniques Artificial Neural Networks and Support Vector Machine are used to predict and classify different natural gas pipeline failures i.e. Corrosion, Pipeline Material, or Weld Failure and Excavation Damage by using actual pipeline incident data. One-Way ANOVA F-test is used to select the important features of the input dataset. The supervised models (Backpropagation Neural Network and SVM) are trained and tested on the input data. The performance of the models is assessed based on accurate predictions made by the trained models on the testing dataset. It is observed that Medium Gaussian SVM integrated with ANOVA (and Holdout cross-validation) performs better than other algorithms and yields 74.8% accuracy. |
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