Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification
Since automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. Fault diagnosis identifies fault types to facilitate maintenance. However, the method of the warning before the fault occurs is more attractive to users and is...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/3/511 |
| _version_ | 1797624828743647232 |
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| author | Xiufeng Li Ning Wang Yelin Lyu Yan Duan Jiaqi Zhao |
| author_facet | Xiufeng Li Ning Wang Yelin Lyu Yan Duan Jiaqi Zhao |
| author_sort | Xiufeng Li |
| collection | DOAJ |
| description | Since automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. Fault diagnosis identifies fault types to facilitate maintenance. However, the method of the warning before the fault occurs is more attractive to users and is more challenging. Therefore, this study would like to explore the feasibility of implementing automobile engine fault early warning based on the fault diagnosis model. First, the theoretical method of a fault domain is established, and the state of the engine is regarded as a point in n-dimensional space. The normal or fault of the engine will correspond to different state domains in this space. Second, to diagnose multiple fault types at the same time, an ensemble model based on multiple machine learning methods is established. The probability outputs by the ensemble model measure the distance between the point and each fault domain in the space. Finally, considering the temporal factor, an early warning threshold is established based on the probability, and a fault warning model is established by using the dual probability structure. Comparative experiments show that the proposed method can greatly reduce the calculation time based on ensuring the accuracy of early warning and is suitable for real-time early warning of multiple faults. |
| first_indexed | 2024-03-11T09:48:12Z |
| format | Article |
| id | doaj.art-80ec284344744365b77fa823e4624dd8 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T09:48:12Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-80ec284344744365b77fa823e4624dd82023-11-16T16:27:30ZengMDPI AGElectronics2079-92922023-01-0112351110.3390/electronics12030511Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft ClassificationXiufeng Li0Ning Wang1Yelin Lyu2Yan Duan3Jiaqi Zhao4School of Automotive Studies, Tongji University, Shanghai 200092, ChinaSchool of Automotive Studies, Tongji University, Shanghai 200092, ChinaSchool of Automotive Studies, Tongji University, Shanghai 200092, ChinaSchool of Automotive Studies, Tongji University, Shanghai 200092, ChinaSchool of Automotive Studies, Tongji University, Shanghai 200092, ChinaSince automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. Fault diagnosis identifies fault types to facilitate maintenance. However, the method of the warning before the fault occurs is more attractive to users and is more challenging. Therefore, this study would like to explore the feasibility of implementing automobile engine fault early warning based on the fault diagnosis model. First, the theoretical method of a fault domain is established, and the state of the engine is regarded as a point in n-dimensional space. The normal or fault of the engine will correspond to different state domains in this space. Second, to diagnose multiple fault types at the same time, an ensemble model based on multiple machine learning methods is established. The probability outputs by the ensemble model measure the distance between the point and each fault domain in the space. Finally, considering the temporal factor, an early warning threshold is established based on the probability, and a fault warning model is established by using the dual probability structure. Comparative experiments show that the proposed method can greatly reduce the calculation time based on ensuring the accuracy of early warning and is suitable for real-time early warning of multiple faults.https://www.mdpi.com/2079-9292/12/3/511automobile enginesoft classificationfault early warningensemble modelpattern recognitionfault detection |
| spellingShingle | Xiufeng Li Ning Wang Yelin Lyu Yan Duan Jiaqi Zhao Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification Electronics automobile engine soft classification fault early warning ensemble model pattern recognition fault detection |
| title | Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification |
| title_full | Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification |
| title_fullStr | Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification |
| title_full_unstemmed | Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification |
| title_short | Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification |
| title_sort | data driven fault early warning model of automobile engines based on soft classification |
| topic | automobile engine soft classification fault early warning ensemble model pattern recognition fault detection |
| url | https://www.mdpi.com/2079-9292/12/3/511 |
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