A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering
Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosi...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/2943 |
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| author | Mohamad Shatnawi Péter Földesi |
| author_facet | Mohamad Shatnawi Péter Földesi |
| author_sort | Mohamad Shatnawi |
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| description | Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties of inspection tools and corrosion processes present in ILI data influence feature matching accuracy. This study proposes a new extensible feature matching model based on consecutive ILIs and data clustering. By dynamically segmenting the data into spatially localized clusters, this framework enables feature matching of isolated pairs and merging defects, as well as facilitating more precise localized transformations. Moreover, a new clustering technique—directional epsilon neighborhood clustering (DENC)—is proposed. DENC utilizes spatial graph structures and directional proximity thresholds to address the directional variability in ILI data while effectively identifying outliers. The model is evaluated on six pipeline segments with varying ILI data complexities, achieving high recall and precision of 91.5% and 98.0%, respectively. In comparison to exclusively point matching models, this work demonstrates significant improvements in terms of accuracy, stability, and managing the spatial variability and interactions of adjacent defects. These advancements establish a new framework for automated feature matching and contribute to enhanced pipeline integrity management. |
| format | Article |
| id | doaj-art-a535867701c847d2b3dfdc430056d36a |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a535867701c847d2b3dfdc430056d36a2025-08-20T02:42:38ZengMDPI AGApplied Sciences2076-34172025-03-01156294310.3390/app15062943A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data ClusteringMohamad Shatnawi0Péter Földesi1Doctoral School of Multidisciplinary Engineering Sciences, Széchenyi István University, 9026 Győr, HungaryDepartment of Logistics, Széchenyi István University, 9026 Győr, HungaryCorrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties of inspection tools and corrosion processes present in ILI data influence feature matching accuracy. This study proposes a new extensible feature matching model based on consecutive ILIs and data clustering. By dynamically segmenting the data into spatially localized clusters, this framework enables feature matching of isolated pairs and merging defects, as well as facilitating more precise localized transformations. Moreover, a new clustering technique—directional epsilon neighborhood clustering (DENC)—is proposed. DENC utilizes spatial graph structures and directional proximity thresholds to address the directional variability in ILI data while effectively identifying outliers. The model is evaluated on six pipeline segments with varying ILI data complexities, achieving high recall and precision of 91.5% and 98.0%, respectively. In comparison to exclusively point matching models, this work demonstrates significant improvements in terms of accuracy, stability, and managing the spatial variability and interactions of adjacent defects. These advancements establish a new framework for automated feature matching and contribute to enhanced pipeline integrity management.https://www.mdpi.com/2076-3417/15/6/2943pipeline integritycorrosion managementdirectional epsilon neighborhood clustering (DENC)affine transformationlinear optimizationlinear programming |
| spellingShingle | Mohamad Shatnawi Péter Földesi A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering Applied Sciences pipeline integrity corrosion management directional epsilon neighborhood clustering (DENC) affine transformation linear optimization linear programming |
| title | A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering |
| title_full | A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering |
| title_fullStr | A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering |
| title_full_unstemmed | A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering |
| title_short | A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering |
| title_sort | new extensible feature matching model for corrosion defects based on consecutive in line inspections and data clustering |
| topic | pipeline integrity corrosion management directional epsilon neighborhood clustering (DENC) affine transformation linear optimization linear programming |
| url | https://www.mdpi.com/2076-3417/15/6/2943 |
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