Prediction of Pipeline Defect Depth and Classification Based on CatBoost.
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| Title: | Prediction of Pipeline Defect Depth and Classification Based on CatBoost. |
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| Authors: | Chen, Cong1 (AUTHOR), Li, Rui2 (AUTHOR), Jia, Guanwei1 (AUTHOR) jiaguanwei@henu.edu.cn, Qin, Dongcun3 (AUTHOR) qindongcun@163.com |
| Source: | Energy Science & Engineering. May2026, Vol. 14 Issue 5, p2429-2439. 11p. |
| Subject Terms: | *Boosting algorithms, *Magnetic flux leakage, *Fault diagnosis, *Machine learning, *Structural health monitoring |
| Abstract: | Magnetic flux leakage detection is a well‐established non‐destructive in‐line inspection for pipelines. In practical applications, the volume of inspection data is large, and manually labeling of defects is time‐consuming and inefficient. Automated processing of inspection data using machine learning methods can address these issues. This study evaluates the performance of four machine learning models in defect classification and defect depth prediction. The synthetic minority oversampling technique (SMOTE) is employed to increase the number of minority class samples, enhancing the model's generalization ability and thereby improving the classification accuracy for minority class defects. The prediction results showed that the Categorical Boosting (CatBoost) model had a defect classification accuracy of 0.9730, a mean squared error of 0.0256 for defect depth prediction, and a prediction residual range of −1 to 1.1 mm. The CatBoost model had the advantages of high classification accuracy, a small prediction error, and a residual range. The study results provided a reference for predicting pipeline defect types and defect depths using machine learning models. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194013633 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prediction of Pipeline Defect Depth and Classification Based on CatBoost. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Cong%22">Chen, Cong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Rui%22">Li, Rui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jia%2C+Guanwei%22">Jia, Guanwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jiaguanwei@henu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Qin%2C+Dongcun%22">Qin, Dongcun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> qindongcun@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energy+Science+%26+Engineering%22">Energy Science & Engineering</searchLink>. May2026, Vol. 14 Issue 5, p2429-2439. 11p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Magnetic+flux+leakage%22">Magnetic flux leakage</searchLink><br />*<searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Structural+health+monitoring%22">Structural health monitoring</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Magnetic flux leakage detection is a well‐established non‐destructive in‐line inspection for pipelines. In practical applications, the volume of inspection data is large, and manually labeling of defects is time‐consuming and inefficient. Automated processing of inspection data using machine learning methods can address these issues. This study evaluates the performance of four machine learning models in defect classification and defect depth prediction. The synthetic minority oversampling technique (SMOTE) is employed to increase the number of minority class samples, enhancing the model's generalization ability and thereby improving the classification accuracy for minority class defects. The prediction results showed that the Categorical Boosting (CatBoost) model had a defect classification accuracy of 0.9730, a mean squared error of 0.0256 for defect depth prediction, and a prediction residual range of −1 to 1.1 mm. The CatBoost model had the advantages of high classification accuracy, a small prediction error, and a residual range. The study results provided a reference for predicting pipeline defect types and defect depths using machine learning models. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194013633 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/ese3.70486 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 2429 Subjects: – SubjectFull: Boosting algorithms Type: general – SubjectFull: Magnetic flux leakage Type: general – SubjectFull: Fault diagnosis Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Structural health monitoring Type: general Titles: – TitleFull: Prediction of Pipeline Defect Depth and Classification Based on CatBoost. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Cong – PersonEntity: Name: NameFull: Li, Rui – PersonEntity: Name: NameFull: Jia, Guanwei – PersonEntity: Name: NameFull: Qin, Dongcun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20500505 Numbering: – Type: volume Value: 14 – Type: issue Value: 5 Titles: – TitleFull: Energy Science & Engineering Type: main |
| ResultId | 1 |
