Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model
As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of mari...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/7 |
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| author | Shuxia Ye Bin Da Liang Qi Han Xiao Shankai Li |
| author_facet | Shuxia Ye Bin Da Liang Qi Han Xiao Shankai Li |
| author_sort | Shuxia Ye |
| collection | DOAJ |
| description | As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, the proposed approach eliminates the need for explicit modeling and leverages a novel optimization algorithm for data denoising. Additionally, a new noise-resistant monitoring index is introduced to enhance monitoring reliability. The paper is structured into two main sections for validation. The first section addresses advanced data preprocessing, where the Improved Sparrow Search Algorithm (ISSA) is employed to optimize the parameters of Random Singular Value Decomposition (RSVD). This step effectively minimizes noise, reduces manual intervention, and handles high-dimensional data. The second section focuses on analyzing the data characteristics using the Random Matrix Theory (RMT) and establishing novel condition monitoring indicators to achieve more reliable monitoring outcomes. The proposed methodology captures the intricate relationships among key variables within the system, providing a more robust framework for condition monitoring. Applied to a marine diesel engine lubrication system, the method demonstrates significant improvements in noise immunity and monitoring reliability. Comparative analyses of condition monitoring models before and after denoising reveal that the relative error of the proposed monitoring index under varying noise amplitudes is within 1%, substantially lower than that of other indices. Furthermore, the monitoring accuracy is improved by 4.95% when the proposed index is employed for system condition monitoring. |
| format | Article |
| id | doaj-art-d770f0f0460d4243beb0563079519c6f |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-d770f0f0460d4243beb0563079519c6f2025-01-24T13:39:06ZengMDPI AGMachines2075-17022024-12-01131710.3390/machines13010007Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition ModelShuxia Ye0Bin Da1Liang Qi2Han Xiao3Shankai Li4School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaAs modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, the proposed approach eliminates the need for explicit modeling and leverages a novel optimization algorithm for data denoising. Additionally, a new noise-resistant monitoring index is introduced to enhance monitoring reliability. The paper is structured into two main sections for validation. The first section addresses advanced data preprocessing, where the Improved Sparrow Search Algorithm (ISSA) is employed to optimize the parameters of Random Singular Value Decomposition (RSVD). This step effectively minimizes noise, reduces manual intervention, and handles high-dimensional data. The second section focuses on analyzing the data characteristics using the Random Matrix Theory (RMT) and establishing novel condition monitoring indicators to achieve more reliable monitoring outcomes. The proposed methodology captures the intricate relationships among key variables within the system, providing a more robust framework for condition monitoring. Applied to a marine diesel engine lubrication system, the method demonstrates significant improvements in noise immunity and monitoring reliability. Comparative analyses of condition monitoring models before and after denoising reveal that the relative error of the proposed monitoring index under varying noise amplitudes is within 1%, substantially lower than that of other indices. Furthermore, the monitoring accuracy is improved by 4.95% when the proposed index is employed for system condition monitoring.https://www.mdpi.com/2075-1702/13/1/7marine diesel enginecondition monitoringdata-driven approachdenoisingrandom matrix theory |
| spellingShingle | Shuxia Ye Bin Da Liang Qi Han Xiao Shankai Li Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model Machines marine diesel engine condition monitoring data-driven approach denoising random matrix theory |
| title | Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model |
| title_full | Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model |
| title_fullStr | Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model |
| title_full_unstemmed | Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model |
| title_short | Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model |
| title_sort | condition monitoring of marine diesel lubrication system based on an optimized random singular value decomposition model |
| topic | marine diesel engine condition monitoring data-driven approach denoising random matrix theory |
| url | https://www.mdpi.com/2075-1702/13/1/7 |
| work_keys_str_mv | AT shuxiaye conditionmonitoringofmarinediesellubricationsystembasedonanoptimizedrandomsingularvaluedecompositionmodel AT binda conditionmonitoringofmarinediesellubricationsystembasedonanoptimizedrandomsingularvaluedecompositionmodel AT liangqi conditionmonitoringofmarinediesellubricationsystembasedonanoptimizedrandomsingularvaluedecompositionmodel AT hanxiao conditionmonitoringofmarinediesellubricationsystembasedonanoptimizedrandomsingularvaluedecompositionmodel AT shankaili conditionmonitoringofmarinediesellubricationsystembasedonanoptimizedrandomsingularvaluedecompositionmodel |