A novel inverse method for Advanced monitoring of lubrication conditions in sliding bearings through adaptive genetic algorithm
This study introduces an inverse lubrication analysis (ILA) method, a novel approach for simulating the lubrication state of sliding bearings under various load conditions. By integrating experimental pressure data from sliding bearings with an adaptive genetic optimization algorithm, this method pr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447925000322 |
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| author | Zhenpeng Wu Bowen Dong Liangyi Nie Adnan Kefal |
| author_facet | Zhenpeng Wu Bowen Dong Liangyi Nie Adnan Kefal |
| author_sort | Zhenpeng Wu |
| collection | DOAJ |
| description | This study introduces an inverse lubrication analysis (ILA) method, a novel approach for simulating the lubrication state of sliding bearings under various load conditions. By integrating experimental pressure data from sliding bearings with an adaptive genetic optimization algorithm, this method precisely calculates the eccentricity, attitude angle, and global pressure distribution of the lubrication film. Unlike traditional forward lubrication analysis (FLA) methods, which indirectly estimate the lubrication film status through loads, the ILA method utilizes direct pressure measurements, ensuring accurate and timely raw data for inverse calculations. This approach rapidly and accurately converts measured data into key parameters, closely aligning simulation results with experimental data. The lubrication states of the experimental sliding bearing under loads of 100 N, 200 N, 300 N, and 400 N were successfully predicted, highlighting the method’s reliability in real-world applications. This study provides a new approach and perspective for health monitoring and fault diagnosis of sliding bearings, especially under extreme conditions. |
| format | Article |
| id | doaj-art-d44de052d126454e9ab1726c4c136963 |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-d44de052d126454e9ab1726c4c1369632025-02-08T05:00:08ZengElsevierAin Shams Engineering Journal2090-44792025-02-01162103291A novel inverse method for Advanced monitoring of lubrication conditions in sliding bearings through adaptive genetic algorithmZhenpeng Wu0Bowen Dong1Liangyi Nie2Adnan Kefal3School of Mechanical and Electrical Engineering, Hubei Polytechnic University, Huangshi 435003 China; Hubei Key Laboratory of Intelligent Conveying Technology and Device, Hubei Polytechnic University, Huangshi 435003 ChinaSchool of Mechanical and Electrical Engineering, Hubei Polytechnic University, Huangshi 435003 China; Hubei Key Laboratory of Intelligent Conveying Technology and Device, Hubei Polytechnic University, Huangshi 435003 China; Key Laboratory of Solidification Control and Digital Preparation Technology, Dalian University of Technology, Dalian 116024 China; Corresponding author.School of Mechanical and Electrical Engineering, Hubei Polytechnic University, Huangshi 435003 China; Hubei Key Laboratory of Intelligent Conveying Technology and Device, Hubei Polytechnic University, Huangshi 435003 ChinaFaculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956 TurkeyThis study introduces an inverse lubrication analysis (ILA) method, a novel approach for simulating the lubrication state of sliding bearings under various load conditions. By integrating experimental pressure data from sliding bearings with an adaptive genetic optimization algorithm, this method precisely calculates the eccentricity, attitude angle, and global pressure distribution of the lubrication film. Unlike traditional forward lubrication analysis (FLA) methods, which indirectly estimate the lubrication film status through loads, the ILA method utilizes direct pressure measurements, ensuring accurate and timely raw data for inverse calculations. This approach rapidly and accurately converts measured data into key parameters, closely aligning simulation results with experimental data. The lubrication states of the experimental sliding bearing under loads of 100 N, 200 N, 300 N, and 400 N were successfully predicted, highlighting the method’s reliability in real-world applications. This study provides a new approach and perspective for health monitoring and fault diagnosis of sliding bearings, especially under extreme conditions.http://www.sciencedirect.com/science/article/pii/S2090447925000322Inverse lubrication analysisSliding bearingsHydrodynamic lubricationAdaptive genetic algorithm |
| spellingShingle | Zhenpeng Wu Bowen Dong Liangyi Nie Adnan Kefal A novel inverse method for Advanced monitoring of lubrication conditions in sliding bearings through adaptive genetic algorithm Ain Shams Engineering Journal Inverse lubrication analysis Sliding bearings Hydrodynamic lubrication Adaptive genetic algorithm |
| title | A novel inverse method for Advanced monitoring of lubrication conditions in sliding bearings through adaptive genetic algorithm |
| title_full | A novel inverse method for Advanced monitoring of lubrication conditions in sliding bearings through adaptive genetic algorithm |
| title_fullStr | A novel inverse method for Advanced monitoring of lubrication conditions in sliding bearings through adaptive genetic algorithm |
| title_full_unstemmed | A novel inverse method for Advanced monitoring of lubrication conditions in sliding bearings through adaptive genetic algorithm |
| title_short | A novel inverse method for Advanced monitoring of lubrication conditions in sliding bearings through adaptive genetic algorithm |
| title_sort | novel inverse method for advanced monitoring of lubrication conditions in sliding bearings through adaptive genetic algorithm |
| topic | Inverse lubrication analysis Sliding bearings Hydrodynamic lubrication Adaptive genetic algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2090447925000322 |
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