Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems
Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches lik...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/12/12/895 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850050800336764928 |
|---|---|
| author | Tingting Wu Hongliang Song Hongli Gao Zongshen Wu Feifei Han |
| author_facet | Tingting Wu Hongliang Song Hongli Gao Zongshen Wu Feifei Han |
| author_sort | Tingting Wu |
| collection | DOAJ |
| description | Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning require high-quality data for fault samples. This study leverages the relative advantages of data mining methods and threshold techniques, proposing an adaptive threshold construction method based on dynamic parameter relationship inference. Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. This significantly enhances fault detection accuracy in marine diesel engine lubrication systems, offering considerable industrial practical value. |
| format | Article |
| id | doaj-art-e9d2568965b243839752bee075c67bf1 |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-e9d2568965b243839752bee075c67bf12025-08-20T02:53:21ZengMDPI AGMachines2075-17022024-12-01121289510.3390/machines12120895Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication SystemsTingting Wu0Hongliang Song1Hongli Gao2Zongshen Wu3Feifei Han4School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaFault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning require high-quality data for fault samples. This study leverages the relative advantages of data mining methods and threshold techniques, proposing an adaptive threshold construction method based on dynamic parameter relationship inference. Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. This significantly enhances fault detection accuracy in marine diesel engine lubrication systems, offering considerable industrial practical value.https://www.mdpi.com/2075-1702/12/12/895diesel enginefault detectionadaptive dynamic thresholdingdata-driven |
| spellingShingle | Tingting Wu Hongliang Song Hongli Gao Zongshen Wu Feifei Han Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems Machines diesel engine fault detection adaptive dynamic thresholding data-driven |
| title | Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems |
| title_full | Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems |
| title_fullStr | Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems |
| title_full_unstemmed | Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems |
| title_short | Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems |
| title_sort | adaptive dynamic thresholding method for fault detection in diesel engine lubrication systems |
| topic | diesel engine fault detection adaptive dynamic thresholding data-driven |
| url | https://www.mdpi.com/2075-1702/12/12/895 |
| work_keys_str_mv | AT tingtingwu adaptivedynamicthresholdingmethodforfaultdetectionindieselenginelubricationsystems AT hongliangsong adaptivedynamicthresholdingmethodforfaultdetectionindieselenginelubricationsystems AT hongligao adaptivedynamicthresholdingmethodforfaultdetectionindieselenginelubricationsystems AT zongshenwu adaptivedynamicthresholdingmethodforfaultdetectionindieselenginelubricationsystems AT feifeihan adaptivedynamicthresholdingmethodforfaultdetectionindieselenginelubricationsystems |