Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions
Abstract This study introduces a method to predict the remaining useful life (RUL) of plain bearings operating under stationary, wear-critical conditions. In this method, the transient wear data of a coupled elastohydrodynamic lubrication (mixed-EHL) and wear simulation approach is used to parametri...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-12-01
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| Series: | Friction |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40544-023-0814-y |
| _version_ | 1827291306381017088 |
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| author | Florian König Florian Wirsing Georg Jacobs Rui He Zhigang Tian Ming J. Zuo |
| author_facet | Florian König Florian Wirsing Georg Jacobs Rui He Zhigang Tian Ming J. Zuo |
| author_sort | Florian König |
| collection | DOAJ |
| description | Abstract This study introduces a method to predict the remaining useful life (RUL) of plain bearings operating under stationary, wear-critical conditions. In this method, the transient wear data of a coupled elastohydrodynamic lubrication (mixed-EHL) and wear simulation approach is used to parametrize a statistical, linear degradation model. The method incorporates Bayesian inference to update the linear degradation model throughout the runtime and thereby consider the transient, system-dependent wear progression within the RUL prediction. A case study is used to show the suitability of the proposed method. The results show that the method can be applied to three distinct types of post-wearing-in behavior: wearing-in with subsequent hydrodynamic, stationary wear, and progressive wear operation. While hydrodynamic operation leads to an infinite lifetime, the method is successfully applied to predict RUL in cases with stationary and progressive wear. |
| first_indexed | 2024-04-24T12:35:43Z |
| format | Article |
| id | doaj.art-0881f7003ab241f4af2a9e081dbaa694 |
| institution | Directory Open Access Journal |
| issn | 2223-7690 2223-7704 |
| language | English |
| last_indexed | 2024-04-24T12:35:43Z |
| publishDate | 2023-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Friction |
| spelling | doaj.art-0881f7003ab241f4af2a9e081dbaa6942024-04-07T11:30:26ZengSpringerOpenFriction2223-76902223-77042023-12-011261272128210.1007/s40544-023-0814-yBayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditionsFlorian König0Florian Wirsing1Georg Jacobs2Rui He3Zhigang Tian4Ming J. Zuo5Institute for Machine Elements and Systems Engineering, RWTH Aachen UniversityInstitute for Machine Elements and Systems Engineering, RWTH Aachen UniversityInstitute for Machine Elements and Systems Engineering, RWTH Aachen UniversityDepartment of Mechanical Engineering, University of AlbertaDepartment of Mechanical Engineering, University of AlbertaDepartment of Mechanical Engineering, University of AlbertaAbstract This study introduces a method to predict the remaining useful life (RUL) of plain bearings operating under stationary, wear-critical conditions. In this method, the transient wear data of a coupled elastohydrodynamic lubrication (mixed-EHL) and wear simulation approach is used to parametrize a statistical, linear degradation model. The method incorporates Bayesian inference to update the linear degradation model throughout the runtime and thereby consider the transient, system-dependent wear progression within the RUL prediction. A case study is used to show the suitability of the proposed method. The results show that the method can be applied to three distinct types of post-wearing-in behavior: wearing-in with subsequent hydrodynamic, stationary wear, and progressive wear operation. While hydrodynamic operation leads to an infinite lifetime, the method is successfully applied to predict RUL in cases with stationary and progressive wear.https://doi.org/10.1007/s40544-023-0814-yplain bearingswear modelingremaining useful life predictionBayesian inference |
| spellingShingle | Florian König Florian Wirsing Georg Jacobs Rui He Zhigang Tian Ming J. Zuo Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions Friction plain bearings wear modeling remaining useful life prediction Bayesian inference |
| title | Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions |
| title_full | Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions |
| title_fullStr | Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions |
| title_full_unstemmed | Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions |
| title_short | Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions |
| title_sort | bayesian inference based wear prediction method for plain bearings under stationary mixed friction conditions |
| topic | plain bearings wear modeling remaining useful life prediction Bayesian inference |
| url | https://doi.org/10.1007/s40544-023-0814-y |
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