Correlative Method for Diagnosing Gas-Turbine Tribological Systems
Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/12/5738 |
| _version_ | 1797592675503833088 |
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| author | Maciej Deliś Sylwester Kłysz Radoslaw Przysowa |
| author_facet | Maciej Deliś Sylwester Kłysz Radoslaw Przysowa |
| author_sort | Maciej Deliś |
| collection | DOAJ |
| description | Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors. |
| first_indexed | 2024-03-11T01:56:30Z |
| format | Article |
| id | doaj.art-e22a142b02dd403e9de119cb10a10d14 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:56:30Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e22a142b02dd403e9de119cb10a10d142023-11-18T12:35:23ZengMDPI AGSensors1424-82202023-06-012312573810.3390/s23125738Correlative Method for Diagnosing Gas-Turbine Tribological SystemsMaciej Deliś0Sylwester Kłysz1Radoslaw Przysowa2Air Force Institute of Technology (ITWL), ul. Ksiecia Boleslawa 6, 01-494 Warsaw, PolandAir Force Institute of Technology (ITWL), ul. Ksiecia Boleslawa 6, 01-494 Warsaw, PolandAir Force Institute of Technology (ITWL), ul. Ksiecia Boleslawa 6, 01-494 Warsaw, PolandLubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors.https://www.mdpi.com/1424-8220/23/12/5738wear debrisoil analysisemission spectroscopyturboproppropeller governorANOVA |
| spellingShingle | Maciej Deliś Sylwester Kłysz Radoslaw Przysowa Correlative Method for Diagnosing Gas-Turbine Tribological Systems Sensors wear debris oil analysis emission spectroscopy turboprop propeller governor ANOVA |
| title | Correlative Method for Diagnosing Gas-Turbine Tribological Systems |
| title_full | Correlative Method for Diagnosing Gas-Turbine Tribological Systems |
| title_fullStr | Correlative Method for Diagnosing Gas-Turbine Tribological Systems |
| title_full_unstemmed | Correlative Method for Diagnosing Gas-Turbine Tribological Systems |
| title_short | Correlative Method for Diagnosing Gas-Turbine Tribological Systems |
| title_sort | correlative method for diagnosing gas turbine tribological systems |
| topic | wear debris oil analysis emission spectroscopy turboprop propeller governor ANOVA |
| url | https://www.mdpi.com/1424-8220/23/12/5738 |
| work_keys_str_mv | AT maciejdelis correlativemethodfordiagnosinggasturbinetribologicalsystems AT sylwesterkłysz correlativemethodfordiagnosinggasturbinetribologicalsystems AT radoslawprzysowa correlativemethodfordiagnosinggasturbinetribologicalsystems |