Corrosion Research of Wet Natural Gathering and Transportation Pipeline Based on SVM

Luo, Zhengshan; Hu, Xiaoli; Gao, Yang

doi:10.1061/9780784413142.102

Cited by 10 publications

(4 citation statements)

References 1 publication

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“…The basic idea of GRA is to determine the closeness of the connection according to the similarity of the geometric shapes of the sequence curves. The closer the shape of the curves, the higher the correlation of the corresponding sequences 23,48 . The method is used to analyze the degree of the influence of each factor on the results.…”

Section: Feature Engineeringmentioning

confidence: 99%

“…Support vector machine (SVR) is also widely used for the corrosion prediction of pipelines. Luo et al 23 established the corrosion prediction model of the wet natural gas gathering and transportation pipeline based on the SVR, BPNN, and multiple regression, respectively. Compared with the the actual data, the average relative error of the corrosion rate obtained by SVM is 11.16%, but 19.54% and 25.32% are obtained by the ANN and multivariate analysis methods, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interpretable machine learning for maximum corrosion depth and influence factor analysis

et al. 2023

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We have employed interpretable methods to uncover the black-box model of the machine learning (ML) for predicting the maximum pitting depth (dmax) of oil and gas pipelines. Ensemble learning (EL) is found to have higher accuracy compared with several classical ML models, and the determination coefficient of the adaptive boosting (AdaBoost) model reaches 0.96 after optimizing the features and hyperparameters. In this work, the running framework of the model was clearly displayed by visualization tool, and Shapley Additive exPlanations (SHAP) values were used to visually interpret the model locally and globally to help understand the predictive logic and the contribution of features. Furthermore, the accumulated local effect (ALE) successfully explains how the features affect the corrosion depth and interact with one another.

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Section: Feature Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning for maximum corrosion depth and influence factor analysis

et al. 2023

View full text Add to dashboard Cite

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“…Han Xiaoming [12] ranked the importance of influencing factors and used an artificial neural network to predict the remaining life and detection cycle of a pipeline. Luo Zhengshan [13] carried out a gray correlation analysis to select important influencing factors and build a corrosion prediction model based on a gray support vector machine. Sun Baocai [14] determined the influencing factors of pipeline failure pressure through a sensitivity analysis and established a GA-BP (L-M) prediction model.…”

Section: Research Status Of the Safety Evaluation Methods For A Marin...mentioning

confidence: 99%

Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network

Sun,

Zhang,

Mukhtar

et al. 2023

Sustainability

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As the lifeline of offshore oil and gas production, a submarine pipeline requires regular safety evaluations with proper maintenance according to the evaluation results. At present, the safety factors based on regional-level commonly used factors in engineering are too many, and this leads to conservative evaluation results with a low acceptance of defects. In this paper, a risk factor evaluation index system for submarine pipeline defects is constructed through an analytic hierarchy process (AHP), and the original safety factors are corrected to achieve accurate evaluations for submarine pipeline safety. By constructing a radial basis neural network (RBFNN), the fast calculation of safety factors for other pipeline defects can be realized. Through comparison, it was found that the values obtained by the machine training were in good agreement with the real values, which reflects the accuracy of the model and provides a basis for the repair of a defective pipeline.

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“…The fuzzy evaluation method of grey theory is to analyze data by using fuzzy mathematics and the grey system analysis method. However, the algorithms for parameter threshold and correlation degree in its membership function have obvious defects, which will have a great impact on the accuracy of the results [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in the Reliability Evaluation of Pipelines for the External Anticorrosion Coating

Zhao

Chen

Fan

et al. 2022

Computational Intelligence and Neuroscience

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The purpose of this research is to enhance the analysis of the reliability status for external anticorrosive coatings. With the limitation and insufficiency of the static evaluation method, we study and construct an evaluation method of dynamic reliability for the anticorrosive layer, integrating the trend analysis of the Markov chain and the set pair theory. This method is implemented by the machine learning software of PyCharm community edition, based on Python language. The algorithm utilizes the connection degree in the set pair theory to determine the risk levels of the anticorrosive coating systems. According to the characteristics of the dynamic change of the anticorrosive layer with time, we built the mathematical evaluation model by combining it with the nonaftereffect property of the Markov chain. Therefore, we can make a dynamic and useful analysis for the reliability grade of the anticorrosive coating and assess the effectiveness grade of the changed reliability for the anticorrosive coating after some time. This method can effectively evaluate the reliability level of the anticorrosion coating through the example of big data of detection points. Under national standards, we provide the theoretical basis for pipeline maintenance within detection cycle requirements.

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Corrosion Research of Wet Natural Gathering and Transportation Pipeline Based on SVM

Cited by 10 publications

References 1 publication

Interpretable machine learning for maximum corrosion depth and influence factor analysis

Interpretable machine learning for maximum corrosion depth and influence factor analysis

Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network

Application of Machine Learning in the Reliability Evaluation of Pipelines for the External Anticorrosion Coating

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