Health Monitoring of Automotive Suspensions: A LSTM Network Approach
In the automotive industry, one of the critical issues is to develop a health monitoring system for condition assessment and remaining fatigue life estimation of key load-bearing components including automotive suspension. However, considering the difficulty to obtain expert knowledge and nonlinear...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6626024 |
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| _version_ | 1849682908877422592 |
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| author | Haoju Hu Huan Luo Xiaoqiang Deng |
| author_facet | Haoju Hu Huan Luo Xiaoqiang Deng |
| author_sort | Haoju Hu |
| collection | DOAJ |
| description | In the automotive industry, one of the critical issues is to develop a health monitoring system for condition assessment and remaining fatigue life estimation of key load-bearing components including automotive suspension. However, considering the difficulty to obtain expert knowledge and nonlinear dynamics in large-scale sensory data, health monitoring of automotive suspension is a challenging work. With the development of deep learning based sequence models in recent years, a long short-term memory (LSTM) network has been proved to capture long-term dependencies in time-series prediction without additional expert knowledge. In this paper, a novel health monitoring system based on a LSTM network is proposed to estimate the remaining fatigue life of automotive suspension. Specifically, first durability tests under various driving cycles are implemented to obtain sequential sensory data provided by common sensors on a test car. Then, a LSTM-based load identification method is designed to predict dynamic stress histories based on the available sensory data. Finally, the damages and remaining fatigue life of the suspensions are estimated by each time step. The experimental results prove that our model can achieve a better performance compared with other representative models. |
| format | Article |
| id | doaj-art-d225ab103b1c464e906abbf53d58d093 |
| institution | DOAJ |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-d225ab103b1c464e906abbf53d58d0932025-08-20T03:24:02ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66260246626024Health Monitoring of Automotive Suspensions: A LSTM Network ApproachHaoju Hu0Huan Luo1Xiaoqiang Deng2GAC Automotive Research & Development Center, Guangzhou 511434, ChinaGAC Automotive Research & Development Center, Guangzhou 511434, ChinaGAC Automotive Research & Development Center, Guangzhou 511434, ChinaIn the automotive industry, one of the critical issues is to develop a health monitoring system for condition assessment and remaining fatigue life estimation of key load-bearing components including automotive suspension. However, considering the difficulty to obtain expert knowledge and nonlinear dynamics in large-scale sensory data, health monitoring of automotive suspension is a challenging work. With the development of deep learning based sequence models in recent years, a long short-term memory (LSTM) network has been proved to capture long-term dependencies in time-series prediction without additional expert knowledge. In this paper, a novel health monitoring system based on a LSTM network is proposed to estimate the remaining fatigue life of automotive suspension. Specifically, first durability tests under various driving cycles are implemented to obtain sequential sensory data provided by common sensors on a test car. Then, a LSTM-based load identification method is designed to predict dynamic stress histories based on the available sensory data. Finally, the damages and remaining fatigue life of the suspensions are estimated by each time step. The experimental results prove that our model can achieve a better performance compared with other representative models.http://dx.doi.org/10.1155/2021/6626024 |
| spellingShingle | Haoju Hu Huan Luo Xiaoqiang Deng Health Monitoring of Automotive Suspensions: A LSTM Network Approach Shock and Vibration |
| title | Health Monitoring of Automotive Suspensions: A LSTM Network Approach |
| title_full | Health Monitoring of Automotive Suspensions: A LSTM Network Approach |
| title_fullStr | Health Monitoring of Automotive Suspensions: A LSTM Network Approach |
| title_full_unstemmed | Health Monitoring of Automotive Suspensions: A LSTM Network Approach |
| title_short | Health Monitoring of Automotive Suspensions: A LSTM Network Approach |
| title_sort | health monitoring of automotive suspensions a lstm network approach |
| url | http://dx.doi.org/10.1155/2021/6626024 |
| work_keys_str_mv | AT haojuhu healthmonitoringofautomotivesuspensionsalstmnetworkapproach AT huanluo healthmonitoringofautomotivesuspensionsalstmnetworkapproach AT xiaoqiangdeng healthmonitoringofautomotivesuspensionsalstmnetworkapproach |