Condition Classification of Fibre Ropes during Cyclic Bend over Sheave testing Using Machine Learning
Fibre ropes have been shown to be a viable alternative to steel wire rope for offshore lifting operations. Visual inspection remains a common method of fibre rope condition monitoring and has the potential to be further automated by machine learning. This would provide a valuable aid to current insp...
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| Format: | Article |
| Language: | English |
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The Prognostics and Health Management Society
2022-01-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: | |
| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/3105 |
| _version_ | 1811272995949248512 |
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| author | Shaun Falconer Peter Krause Thomas Bäck Ellen Nordgård-Hansen Geir Grasmo |
| author_facet | Shaun Falconer Peter Krause Thomas Bäck Ellen Nordgård-Hansen Geir Grasmo |
| author_sort | Shaun Falconer |
| collection | DOAJ |
| description | Fibre ropes have been shown to be a viable alternative to steel wire rope for offshore lifting operations. Visual inspection remains a common method of fibre rope condition monitoring and has the potential to be further automated by machine learning. This would provide a valuable aid to current inspection frameworks to make more accurate decisions on recertification or retirement of fibre ropes in operational use. Three different machine learning algorithms: decision tree, random forest and support vector machine are compared to classical statistical approaches such as logistic regression, k-nearest neighbours and Naïve-Bayes for condition classification for fibre ropes under cyclic-bend-over-sheave (CBOS) testing. By measuring the rope global elongation throughout the CBOS tests, a binary classification system has been used to label recorded samples as healthy or close to rupture. Predictions are made on one rope through leave-one-out cross validation. The models are then assessed through calculating the accuracy, probability of detection, probability of false alarm and Matthew’s Correlation Coefficient, and ranked based on the results. The results show that both machine learning and classical statistical methods are effective options for condition classification of fibre ropes under CBOS regimes. Typical values for Matthews Correlation Coefficient (MCC) were shown to exceed 0.8 for the best performing methods. |
| first_indexed | 2024-04-12T22:51:18Z |
| format | Article |
| id | doaj.art-7c9b526687c442229310882fc6370166 |
| institution | Directory Open Access Journal |
| issn | 2153-2648 |
| language | English |
| last_indexed | 2024-04-12T22:51:18Z |
| publishDate | 2022-01-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj.art-7c9b526687c442229310882fc63701662022-12-22T03:13:20ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482022-01-01Vol. 13No.1https://doi.org/10.36001/ijphm.2022.v13i1.3105Condition Classification of Fibre Ropes during Cyclic Bend over Sheave testing Using Machine LearningShaun Falconer0Peter Krause1Thomas Bäck2Ellen Nordgård-Hansen3Geir Grasmo4Department of Engineering Sciences, University of Agder, Grimstad 4876, NorwayDivis Intelligent Solutions Gmbh, Dortmund 44227, GermanyLeiden Institute of Advanced Computer Science, Leiden University, Leiden 2333 CA, NetherlandsNORCE Norwegian Research Centre A/S, Grimstad 4876, NorwayDepartment of Engineering Sciences, University of Agder, Grimstad 4876, NorwayFibre ropes have been shown to be a viable alternative to steel wire rope for offshore lifting operations. Visual inspection remains a common method of fibre rope condition monitoring and has the potential to be further automated by machine learning. This would provide a valuable aid to current inspection frameworks to make more accurate decisions on recertification or retirement of fibre ropes in operational use. Three different machine learning algorithms: decision tree, random forest and support vector machine are compared to classical statistical approaches such as logistic regression, k-nearest neighbours and Naïve-Bayes for condition classification for fibre ropes under cyclic-bend-over-sheave (CBOS) testing. By measuring the rope global elongation throughout the CBOS tests, a binary classification system has been used to label recorded samples as healthy or close to rupture. Predictions are made on one rope through leave-one-out cross validation. The models are then assessed through calculating the accuracy, probability of detection, probability of false alarm and Matthew’s Correlation Coefficient, and ranked based on the results. The results show that both machine learning and classical statistical methods are effective options for condition classification of fibre ropes under CBOS regimes. Typical values for Matthews Correlation Coefficient (MCC) were shown to exceed 0.8 for the best performing methods.https://papers.phmsociety.org/index.php/ijphm/article/view/3105fibre ropecondition monitoringmachine learningdecision treesrandom forestsupport vector machines |
| spellingShingle | Shaun Falconer Peter Krause Thomas Bäck Ellen Nordgård-Hansen Geir Grasmo Condition Classification of Fibre Ropes during Cyclic Bend over Sheave testing Using Machine Learning International Journal of Prognostics and Health Management fibre rope condition monitoring machine learning decision trees random forest support vector machines |
| title | Condition Classification of Fibre Ropes during Cyclic Bend over Sheave testing Using Machine Learning |
| title_full | Condition Classification of Fibre Ropes during Cyclic Bend over Sheave testing Using Machine Learning |
| title_fullStr | Condition Classification of Fibre Ropes during Cyclic Bend over Sheave testing Using Machine Learning |
| title_full_unstemmed | Condition Classification of Fibre Ropes during Cyclic Bend over Sheave testing Using Machine Learning |
| title_short | Condition Classification of Fibre Ropes during Cyclic Bend over Sheave testing Using Machine Learning |
| title_sort | condition classification of fibre ropes during cyclic bend over sheave testing using machine learning |
| topic | fibre rope condition monitoring machine learning decision trees random forest support vector machines |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/3105 |
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