Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench
Improving the reliability of blade bearings is essential for the safe operation of wind turbines. This challenge can be met with the help of virtual testing and digital-twin driven condition monitoring. For such approaches, a precise digital representation of the blade bearing and its test bench is...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001022 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850251193757990912 |
|---|---|
| author | Luca Faller Matthis Graßmann Timo Lichtenstein |
| author_facet | Luca Faller Matthis Graßmann Timo Lichtenstein |
| author_sort | Luca Faller |
| collection | DOAJ |
| description | Improving the reliability of blade bearings is essential for the safe operation of wind turbines. This challenge can be met with the help of virtual testing and digital-twin driven condition monitoring. For such approaches, a precise digital representation of the blade bearing and its test bench is an essential prerequisite. However, various factors prevent the capture of all parameters of the blade bearing and the associated test bench. Parameters such as bearing preload, rolling element and raceway dimensions, and bolt preload during assembly vary with each bearing and test bench setup. As these parameters cannot be measured directly, an alternative solution is required. This article presents a methodology to efficiently estimate non-measurable parameters of the test bench using a combination of model-based and data-driven approaches, improving the detailed and accurate virtual testing of blade bearings. It must be ensured to enable the fastest possible, most computationally efficient estimation of parameters during virtual testing or condition monitoring. The developed methodology is evaluated using the example of bolt preload on the test bench. By employing a random forest model and the strain gauge measurements attached to the blade bearing, the bolt preload parameters are estimated. The results demonstrate that the accuracy of the digital model of the blade bearing test bench is improved by up to 11 % in three out of four test bench setups. The great improvement in the accuracy of the digital model highlights the effectiveness of the proposed methodology in enhancing virtual blade bearing testing and digital-twin driven condition monitoring. |
| format | Article |
| id | doaj-art-6adeba89ad0248869c5aefb50b00b384 |
| institution | OA Journals |
| issn | 2666-5468 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-6adeba89ad0248869c5aefb50b00b3842025-08-20T01:57:59ZengElsevierEnergy and AI2666-54682024-12-011810043610.1016/j.egyai.2024.100436Machine learning based parameter estimation for an adapted finite element model of a blade bearing test benchLuca Faller0Matthis Graßmann1Timo Lichtenstein2Fraunhofer Institute for Wind Energy Systems IWES, Postkamp 12, 30159, Hannover, Germany; Corresponding author.Fraunhofer Institute for Wind Energy Systems IWES, Am Schleusengraben 22, 21029, Hamburg, GermanyFraunhofer Institute for Wind Energy Systems IWES, Postkamp 12, 30159, Hannover, GermanyImproving the reliability of blade bearings is essential for the safe operation of wind turbines. This challenge can be met with the help of virtual testing and digital-twin driven condition monitoring. For such approaches, a precise digital representation of the blade bearing and its test bench is an essential prerequisite. However, various factors prevent the capture of all parameters of the blade bearing and the associated test bench. Parameters such as bearing preload, rolling element and raceway dimensions, and bolt preload during assembly vary with each bearing and test bench setup. As these parameters cannot be measured directly, an alternative solution is required. This article presents a methodology to efficiently estimate non-measurable parameters of the test bench using a combination of model-based and data-driven approaches, improving the detailed and accurate virtual testing of blade bearings. It must be ensured to enable the fastest possible, most computationally efficient estimation of parameters during virtual testing or condition monitoring. The developed methodology is evaluated using the example of bolt preload on the test bench. By employing a random forest model and the strain gauge measurements attached to the blade bearing, the bolt preload parameters are estimated. The results demonstrate that the accuracy of the digital model of the blade bearing test bench is improved by up to 11 % in three out of four test bench setups. The great improvement in the accuracy of the digital model highlights the effectiveness of the proposed methodology in enhancing virtual blade bearing testing and digital-twin driven condition monitoring.http://www.sciencedirect.com/science/article/pii/S2666546824001022Wind energyBlade bearingMachine learningVirtual testingDigital twinHybrid models |
| spellingShingle | Luca Faller Matthis Graßmann Timo Lichtenstein Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench Energy and AI Wind energy Blade bearing Machine learning Virtual testing Digital twin Hybrid models |
| title | Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench |
| title_full | Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench |
| title_fullStr | Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench |
| title_full_unstemmed | Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench |
| title_short | Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench |
| title_sort | machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench |
| topic | Wind energy Blade bearing Machine learning Virtual testing Digital twin Hybrid models |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001022 |
| work_keys_str_mv | AT lucafaller machinelearningbasedparameterestimationforanadaptedfiniteelementmodelofabladebearingtestbench AT matthisgraßmann machinelearningbasedparameterestimationforanadaptedfiniteelementmodelofabladebearingtestbench AT timolichtenstein machinelearningbasedparameterestimationforanadaptedfiniteelementmodelofabladebearingtestbench |