Predicting the Wear Amount of Tire Tread Using 1D−CNN
Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to v...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/6901 |
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| author | Hyunjae Park Junyeong Seo Kangjun Kim Taewung Kim |
| author_facet | Hyunjae Park Junyeong Seo Kangjun Kim Taewung Kim |
| author_sort | Hyunjae Park |
| collection | DOAJ |
| description | Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to validate the method. First, driving tests were conducted with tires at various wear levels to measure internal accelerations. The acceleration signals were then screened using empirical functions to exclude atypical data before proceeding with the machine learning process. Finally, a tire wear prediction algorithm based on a 1D−CNN with bottleneck features was developed and evaluated. The developed algorithm showed an RMSE of 5.2% (or 0.42 mm) using only the acceleration signals. When tire pressure and vertical load were included, the prediction error was reduced by 11.5%, resulting in an RMSE of 4.6%. These findings suggest that the 1D−CNN approach is an efficient method for predicting tire wear states, requiring minimal input data. Additionally, it supports the potential usefulness of the intelligent tire technology framework proposed in the modeling study. |
| format | Article |
| id | doaj-art-55a922db8c104af6b424073c59d45e7f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-55a922db8c104af6b424073c59d45e7f2024-11-08T14:41:21ZengMDPI AGSensors1424-82202024-10-012421690110.3390/s24216901Predicting the Wear Amount of Tire Tread Using 1D−CNNHyunjae Park0Junyeong Seo1Kangjun Kim2Taewung Kim3Department of Mechanical Design Engineering, Tech University of Korea, Siheung-si 15073, Gyeonggi-do, Republic of KoreaDepartment of Mechanical Design Engineering, Tech University of Korea, Siheung-si 15073, Gyeonggi-do, Republic of KoreaDepartment of Mechanical Design Engineering, Tech University of Korea, Siheung-si 15073, Gyeonggi-do, Republic of KoreaDepartment of Mechanical Design Engineering, Tech University of Korea, Siheung-si 15073, Gyeonggi-do, Republic of KoreaSince excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to validate the method. First, driving tests were conducted with tires at various wear levels to measure internal accelerations. The acceleration signals were then screened using empirical functions to exclude atypical data before proceeding with the machine learning process. Finally, a tire wear prediction algorithm based on a 1D−CNN with bottleneck features was developed and evaluated. The developed algorithm showed an RMSE of 5.2% (or 0.42 mm) using only the acceleration signals. When tire pressure and vertical load were included, the prediction error was reduced by 11.5%, resulting in an RMSE of 4.6%. These findings suggest that the 1D−CNN approach is an efficient method for predicting tire wear states, requiring minimal input data. Additionally, it supports the potential usefulness of the intelligent tire technology framework proposed in the modeling study.https://www.mdpi.com/1424-8220/24/21/6901tire wear prediction1D−CNNbottleneck featurestire internal accelerationtire internal pressuretire vertical load |
| spellingShingle | Hyunjae Park Junyeong Seo Kangjun Kim Taewung Kim Predicting the Wear Amount of Tire Tread Using 1D−CNN Sensors tire wear prediction 1D−CNN bottleneck features tire internal acceleration tire internal pressure tire vertical load |
| title | Predicting the Wear Amount of Tire Tread Using 1D−CNN |
| title_full | Predicting the Wear Amount of Tire Tread Using 1D−CNN |
| title_fullStr | Predicting the Wear Amount of Tire Tread Using 1D−CNN |
| title_full_unstemmed | Predicting the Wear Amount of Tire Tread Using 1D−CNN |
| title_short | Predicting the Wear Amount of Tire Tread Using 1D−CNN |
| title_sort | predicting the wear amount of tire tread using 1d cnn |
| topic | tire wear prediction 1D−CNN bottleneck features tire internal acceleration tire internal pressure tire vertical load |
| url | https://www.mdpi.com/1424-8220/24/21/6901 |
| work_keys_str_mv | AT hyunjaepark predictingthewearamountoftiretreadusing1dcnn AT junyeongseo predictingthewearamountoftiretreadusing1dcnn AT kangjunkim predictingthewearamountoftiretreadusing1dcnn AT taewungkim predictingthewearamountoftiretreadusing1dcnn |