Innovative prognostic methodology for pipe defect detection leveraging acoustic emissions analysis and computational modeling integration.
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| Title: | Innovative prognostic methodology for pipe defect detection leveraging acoustic emissions analysis and computational modeling integration. |
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| Authors: | Braydi, Ahmad1,2 (AUTHOR) ahmad.braydi@ec-lyon.fr, Fossat, Pascal1 (AUTHOR) pascal.fossat@ec-lyon.fr, Ardabilian, Mohsen2 (AUTHOR) mohsen.ardabilian@ec-lyon.fr, Bareille, Olivier1,3 (AUTHOR) olivier.bareille@ec-lyon.fr |
| Source: | Applied Ocean Research. May2025, Vol. 158, pN.PAG-N.PAG. 1p. |
| Subjects: | Acoustic emission testing, Acoustic wave propagation, Machine learning, Finite element method, Environmental infrastructure |
| Abstract: | Pipelines play a crucial role in transporting essential resources such as water, oil, and gas across industrial, urban, and environmental infrastructures. Clogging remains a persistent challenge, potentially resulting in catastrophic failures, operational disruptions, increased maintenance costs, and serious safety risks. This study presents a novel prognostic and health monitoring approach that utilizes bubble-induced acoustic emissions to detect and characterize pipeline blockages. An analytical model is developed to capture the acoustic signatures of detaching bubbles, revealing features highly sensitive to clogging. Finite element simulations using Abaqus further investigate how different clogging conditions affect acoustic wave propagation. These insights drive the development of a machine learning-based predictive maintenance strategy, validated on real-world datasets. The results demonstrate exceptional accuracy, with most classifiers achieving 100% detection rates for clogging presence, shape, and severity. Additionally, model generalization tests show that machine learning algorithms adapt more effectively to varying clogging thickness than clogging shape. This research paves the way for a highly accurate, non-destructive monitoring solution, enhancing predictive maintenance and ensuring the reliability of industrial pipelines. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Ocean Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 185226076 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Innovative prognostic methodology for pipe defect detection leveraging acoustic emissions analysis and computational modeling integration. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Braydi%2C+Ahmad%22">Braydi, Ahmad</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ahmad.braydi@ec-lyon.fr</i><br /><searchLink fieldCode="AR" term="%22Fossat%2C+Pascal%22">Fossat, Pascal</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pascal.fossat@ec-lyon.fr</i><br /><searchLink fieldCode="AR" term="%22Ardabilian%2C+Mohsen%22">Ardabilian, Mohsen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mohsen.ardabilian@ec-lyon.fr</i><br /><searchLink fieldCode="AR" term="%22Bareille%2C+Olivier%22">Bareille, Olivier</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> olivier.bareille@ec-lyon.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Ocean+Research%22">Applied Ocean Research</searchLink>. May2025, Vol. 158, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Acoustic+emission+testing%22">Acoustic emission testing</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustic+wave+propagation%22">Acoustic wave propagation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+infrastructure%22">Environmental infrastructure</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Pipelines play a crucial role in transporting essential resources such as water, oil, and gas across industrial, urban, and environmental infrastructures. Clogging remains a persistent challenge, potentially resulting in catastrophic failures, operational disruptions, increased maintenance costs, and serious safety risks. This study presents a novel prognostic and health monitoring approach that utilizes bubble-induced acoustic emissions to detect and characterize pipeline blockages. An analytical model is developed to capture the acoustic signatures of detaching bubbles, revealing features highly sensitive to clogging. Finite element simulations using Abaqus further investigate how different clogging conditions affect acoustic wave propagation. These insights drive the development of a machine learning-based predictive maintenance strategy, validated on real-world datasets. The results demonstrate exceptional accuracy, with most classifiers achieving 100% detection rates for clogging presence, shape, and severity. Additionally, model generalization tests show that machine learning algorithms adapt more effectively to varying clogging thickness than clogging shape. This research paves the way for a highly accurate, non-destructive monitoring solution, enhancing predictive maintenance and ensuring the reliability of industrial pipelines. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Ocean Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.apor.2025.104521 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Acoustic emission testing Type: general – SubjectFull: Acoustic wave propagation Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Finite element method Type: general – SubjectFull: Environmental infrastructure Type: general Titles: – TitleFull: Innovative prognostic methodology for pipe defect detection leveraging acoustic emissions analysis and computational modeling integration. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Braydi, Ahmad – PersonEntity: Name: NameFull: Fossat, Pascal – PersonEntity: Name: NameFull: Ardabilian, Mohsen – PersonEntity: Name: NameFull: Bareille, Olivier IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 01411187 Numbering: – Type: volume Value: 158 Titles: – TitleFull: Applied Ocean Research Type: main |
| ResultId | 1 |
