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| Auteur principal: | |
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| Format: | Artículo científico |
| Langue: | en |
| Publié: |
Universidad Tecnológica de Pereira
2021
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| Sujets: | |
| Accès en ligne: | https://www.redalyc.org/articulo.oa?id=84969892004 https://www.redalyc.org/journal/849/84969892004/ https://www.redalyc.org/journal/849/84969892004/html/ https://www.redalyc.org/journal/849/84969892004/84969892004.epub https://www.redalyc.org/journal/849/84969892004/movil https://doi.org/10.22517/23447214.24579 |
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| _version_ | 1866815725810745344 |
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| author | Juan C. Mejia |
| author_facet | Juan C. Mejia |
| contents | Use of Multiscale Permutation Entropy Feature Selection and Supervised Classifiers for Bearing Failures Diagnosis Juan C. Mejia Héctor Fabio Quintero Riaza Julian D. Echeverry-Correa Ingeniería Entropy Machine Dynamics Vibration Multiescale Entropy measurements are an accessible tool to perform irregularity and uncertainty measurements present in time series. In signal processing, the Multiscale Permutation Entropy is recently presented as a methodology of characterization capable of measuring randomness and non-linear dynamics existing in non-stationary time series, such as mechanical vibration signals. In this article, the Multiscale Permutation Entropy is combined with diverse feature selection techniques and multiple classifiers based on machine learning aiming to detect different operative states in an internal combustion engine. The best combination is selected from the evaluation of parameters like precision and computational time. Finally, the proposed methodology is established as an effective tool to diagnose failures in bearing systems with a high precision rate and a reduced calculation time. 2021 artículo científico 0122-1701 https://www.redalyc.org/articulo.oa?id=84969892004 https://www.redalyc.org/journal/849/84969892004/ https://www.redalyc.org/journal/849/84969892004/html/ https://www.redalyc.org/journal/849/84969892004/84969892004.epub https://www.redalyc.org/journal/849/84969892004/movil https://doi.org/10.22517/23447214.24579 en http://www.redalyc.org/revista.oa?id=849 Scientia Et Technica application/pdf Universidad Tecnológica de Pereira Scientia Et Technica (Colombia) Num.4 Vol.26 |
| format | Artículo científico |
| id | redalyc_84969892004 |
| language | en |
| publishDate | 2021 |
| publisher | Universidad Tecnológica de Pereira |
| spellingShingle | Use of Multiscale Permutation Entropy Feature Selection and Supervised Classifiers for Bearing Failures Diagnosis Juan C. Mejia Ingeniería Entropy Machine Dynamics Vibration Multiescale Use of Multiscale Permutation Entropy Feature Selection and Supervised Classifiers for Bearing Failures Diagnosis Juan C. Mejia Héctor Fabio Quintero Riaza Julian D. Echeverry-Correa Ingeniería Entropy Machine Dynamics Vibration Multiescale Entropy measurements are an accessible tool to perform irregularity and uncertainty measurements present in time series. In signal processing, the Multiscale Permutation Entropy is recently presented as a methodology of characterization capable of measuring randomness and non-linear dynamics existing in non-stationary time series, such as mechanical vibration signals. In this article, the Multiscale Permutation Entropy is combined with diverse feature selection techniques and multiple classifiers based on machine learning aiming to detect different operative states in an internal combustion engine. The best combination is selected from the evaluation of parameters like precision and computational time. Finally, the proposed methodology is established as an effective tool to diagnose failures in bearing systems with a high precision rate and a reduced calculation time. 2021 artículo científico 0122-1701 https://www.redalyc.org/articulo.oa?id=84969892004 https://www.redalyc.org/journal/849/84969892004/ https://www.redalyc.org/journal/849/84969892004/html/ https://www.redalyc.org/journal/849/84969892004/84969892004.epub https://www.redalyc.org/journal/849/84969892004/movil https://doi.org/10.22517/23447214.24579 en http://www.redalyc.org/revista.oa?id=849 Scientia Et Technica application/pdf Universidad Tecnológica de Pereira Scientia Et Technica (Colombia) Num.4 Vol.26 |
| title | Use of Multiscale Permutation Entropy Feature Selection and Supervised Classifiers for Bearing Failures Diagnosis |
| topic | Ingeniería Entropy Machine Dynamics Vibration Multiescale |
| url | https://www.redalyc.org/articulo.oa?id=84969892004 https://www.redalyc.org/journal/849/84969892004/ https://www.redalyc.org/journal/849/84969892004/html/ https://www.redalyc.org/journal/849/84969892004/84969892004.epub https://www.redalyc.org/journal/849/84969892004/movil https://doi.org/10.22517/23447214.24579 |