Diesel engine diagnosis based on entropy of vibration signals and machine learning techniques
Abstract Compression‐ignition (CI) engines, aka diesel engines, are responsible for an essential percentage of the world‐polluting emissions. Moreover, bearings installed in industrial machinery constitute the most common failure affecting global energy consumption. Since industries’ energy demand h...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-05-01
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| Series: | Electronics Letters |
| Online Access: | https://doi.org/10.1049/ell2.12490 |
| _version_ | 1811340173929086976 |
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| author | Juan Camilo Mejía Hernández Federico Gutiérrez Madrid Héctor Fabio Quintero Juan David Ramírez Alzate |
| author_facet | Juan Camilo Mejía Hernández Federico Gutiérrez Madrid Héctor Fabio Quintero Juan David Ramírez Alzate |
| author_sort | Juan Camilo Mejía Hernández |
| collection | DOAJ |
| description | Abstract Compression‐ignition (CI) engines, aka diesel engines, are responsible for an essential percentage of the world‐polluting emissions. Moreover, bearings installed in industrial machinery constitute the most common failure affecting global energy consumption. Since industries’ energy demand has a growing tendency, efficient maintenance is a must. Maintenance requires a fast and accurate diagnosis, commonly based on an intrusive or expensive sensor to capture monitoring signals, i.e. pressure, emissions, temperature, fuel consumption and rotational speed. Here, a vibration signal‐based approach is introduced to combustion engines and bearings diagnosis. Namely, a multi‐scale permutation entropy (MPE)‐based feature extraction is conducted within a variability‐based relevance analysis (VRA) stage to feed a straightforward classifier, the K‐nearest neighbours (KNN). Accuracy was validated using a signals’ database from a single‐cylinder engine under multiple work conditions. Also, the methodology is compared through classification accuracy of a widely known bearing vibration signal database obtaining an outstanding performance. |
| first_indexed | 2024-04-13T18:38:05Z |
| format | Article |
| id | doaj.art-8be8e844d770444da36f83fd53303fd7 |
| institution | Directory Open Access Journal |
| issn | 0013-5194 1350-911X |
| language | English |
| last_indexed | 2024-04-13T18:38:05Z |
| publishDate | 2022-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj.art-8be8e844d770444da36f83fd53303fd72022-12-22T02:34:49ZengWileyElectronics Letters0013-51941350-911X2022-05-01581144244410.1049/ell2.12490Diesel engine diagnosis based on entropy of vibration signals and machine learning techniquesJuan Camilo Mejía Hernández0Federico Gutiérrez Madrid1Héctor Fabio Quintero2Juan David Ramírez Alzate3Universidad Tecnológica de Pereira Pereira ColombiaUniversidad Tecnológica de Pereira Pereira ColombiaUniversidad Tecnológica de Pereira Pereira ColombiaUniversidad Tecnológica de Pereira Pereira ColombiaAbstract Compression‐ignition (CI) engines, aka diesel engines, are responsible for an essential percentage of the world‐polluting emissions. Moreover, bearings installed in industrial machinery constitute the most common failure affecting global energy consumption. Since industries’ energy demand has a growing tendency, efficient maintenance is a must. Maintenance requires a fast and accurate diagnosis, commonly based on an intrusive or expensive sensor to capture monitoring signals, i.e. pressure, emissions, temperature, fuel consumption and rotational speed. Here, a vibration signal‐based approach is introduced to combustion engines and bearings diagnosis. Namely, a multi‐scale permutation entropy (MPE)‐based feature extraction is conducted within a variability‐based relevance analysis (VRA) stage to feed a straightforward classifier, the K‐nearest neighbours (KNN). Accuracy was validated using a signals’ database from a single‐cylinder engine under multiple work conditions. Also, the methodology is compared through classification accuracy of a widely known bearing vibration signal database obtaining an outstanding performance.https://doi.org/10.1049/ell2.12490 |
| spellingShingle | Juan Camilo Mejía Hernández Federico Gutiérrez Madrid Héctor Fabio Quintero Juan David Ramírez Alzate Diesel engine diagnosis based on entropy of vibration signals and machine learning techniques Electronics Letters |
| title | Diesel engine diagnosis based on entropy of vibration signals and machine learning techniques |
| title_full | Diesel engine diagnosis based on entropy of vibration signals and machine learning techniques |
| title_fullStr | Diesel engine diagnosis based on entropy of vibration signals and machine learning techniques |
| title_full_unstemmed | Diesel engine diagnosis based on entropy of vibration signals and machine learning techniques |
| title_short | Diesel engine diagnosis based on entropy of vibration signals and machine learning techniques |
| title_sort | diesel engine diagnosis based on entropy of vibration signals and machine learning techniques |
| url | https://doi.org/10.1049/ell2.12490 |
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