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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.08757 |
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| _version_ | 1866915192353325056 |
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| author | Arizmendi, C J Garcia, W L Quintero, M A |
| author_facet | Arizmendi, C J Garcia, W L Quintero, M A |
| contents | This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called smart pig in Oil and Gas pipelines . The model uses a signal noise reduction phase by means of preprocessing algorithms and attributeselection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08757 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic welding detection by an intelligent tool pipe inspection Arizmendi, C J Garcia, W L Quintero, M A Machine Learning This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called smart pig in Oil and Gas pipelines . The model uses a signal noise reduction phase by means of preprocessing algorithms and attributeselection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent |
| title | Automatic welding detection by an intelligent tool pipe inspection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.08757 |