Engine Health Status Prediction Based on Oil Analysis With Augmented Machine Learning Algorithms
Tribology is the very efficient and strong tool in machine operations analysis. In the article author presented how the artificial intelligence algorithms could be applied to help in engine oil test results analysis. Based on the real-life turbofan engine oil sample test results dataset, the novel m...
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2024-12-01
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| Series: | Tribology in Industry |
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| Online Access: | https://www.tribology.rs/journals/2024/2024-4/2024-4-07.html |
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| _version_ | 1841553379992535040 |
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| author | Slawomir Szrama |
| author_facet | Slawomir Szrama |
| author_sort | Slawomir Szrama |
| collection | DOAJ |
| description | Tribology is the very efficient and strong tool in machine operations analysis. In the article author presented how the artificial intelligence algorithms could be applied to help in engine oil test results analysis. Based on the real-life turbofan engine oil sample test results dataset, the novel methodology of the machine learning algorithm implementation was presented. In order to take advantage of the artificial intelligence in engine oil test results interpretation, the augmented engine oil dataset was generated with additional predictors. Research case study was conducted for both original engine dataset as well as the augmented one. For the scientific purposed, various machine learning performance metrics were calculated, what allowed to precisely compare the results achieved for the original dataset and the one generated on the basis of the proposed novel method. The greatest achievement of the article was the presentation of the new methodology implementation in the real-life turbofan engine health status prediction. Presented methodology implemented into the aircraft (engine) maintenance management computer system allows to automate engine health status analysis and improve engine maintenance management. |
| format | Article |
| id | doaj-art-61a4371ba758493abfa1881a8d07fa24 |
| institution | Kabale University |
| issn | 0354-8996 2217-7965 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Tribology in Industry |
| spelling | doaj-art-61a4371ba758493abfa1881a8d07fa242025-01-09T10:24:44ZengUniversity of KragujevacTribology in Industry0354-89962217-79652024-12-0146462463810.24874/ti.1736.08.24.09Engine Health Status Prediction Based on Oil Analysis With Augmented Machine Learning AlgorithmsSlawomir Szrama0https://orcid.org/0000-0003-0729-071XPoznan University of Technology, Aviation Division, 60-965 Poznan, Piotrowo 3, PolandTribology is the very efficient and strong tool in machine operations analysis. In the article author presented how the artificial intelligence algorithms could be applied to help in engine oil test results analysis. Based on the real-life turbofan engine oil sample test results dataset, the novel methodology of the machine learning algorithm implementation was presented. In order to take advantage of the artificial intelligence in engine oil test results interpretation, the augmented engine oil dataset was generated with additional predictors. Research case study was conducted for both original engine dataset as well as the augmented one. For the scientific purposed, various machine learning performance metrics were calculated, what allowed to precisely compare the results achieved for the original dataset and the one generated on the basis of the proposed novel method. The greatest achievement of the article was the presentation of the new methodology implementation in the real-life turbofan engine health status prediction. Presented methodology implemented into the aircraft (engine) maintenance management computer system allows to automate engine health status analysis and improve engine maintenance management.https://www.tribology.rs/journals/2024/2024-4/2024-4-07.htmlaircraft turbofan engineengine health status prediction artificial neural networkturbofan engine lubrication systemengine diagnostics and health monitoringengine oil samplingmachine learning |
| spellingShingle | Slawomir Szrama Engine Health Status Prediction Based on Oil Analysis With Augmented Machine Learning Algorithms Tribology in Industry aircraft turbofan engine engine health status prediction artificial neural network turbofan engine lubrication system engine diagnostics and health monitoring engine oil sampling machine learning |
| title | Engine Health Status Prediction Based on Oil Analysis With Augmented Machine Learning Algorithms |
| title_full | Engine Health Status Prediction Based on Oil Analysis With Augmented Machine Learning Algorithms |
| title_fullStr | Engine Health Status Prediction Based on Oil Analysis With Augmented Machine Learning Algorithms |
| title_full_unstemmed | Engine Health Status Prediction Based on Oil Analysis With Augmented Machine Learning Algorithms |
| title_short | Engine Health Status Prediction Based on Oil Analysis With Augmented Machine Learning Algorithms |
| title_sort | engine health status prediction based on oil analysis with augmented machine learning algorithms |
| topic | aircraft turbofan engine engine health status prediction artificial neural network turbofan engine lubrication system engine diagnostics and health monitoring engine oil sampling machine learning |
| url | https://www.tribology.rs/journals/2024/2024-4/2024-4-07.html |
| work_keys_str_mv | AT slawomirszrama enginehealthstatuspredictionbasedonoilanalysiswithaugmentedmachinelearningalgorithms |