An estimation algorithm for tire wear using intelligent tire concept

Li, Bo; Quan, Zhenqiang; Bei, Shaoyi; Zhang, Lanchun; Mao, Haijian

doi:10.1177/0954407021999483

Cited by 22 publications

(11 citation statements)

References 12 publications

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“…The current study has four main differences from the previous tire wear prediction studies: consideration of various sensing options, the use of a 1D-CNN model with a bottleneck structure, the inclusion of an additional test, and the use of the finite element modeling approach [ 4 , 5 , 6 , 7 ].…”

Section: Discussionmentioning

confidence: 99%

“…To assess the generalization performance of the tire wear algorithms, a second test was conducted using a data set (Test2) that was distinct from the training and validation data sets in terms of tire wear amounts ( Table 1 and Figure 5 b). In contrast, previous studies used data from the tires with the same sets of tire wear amounts for training, validation, and test [ 4 , 5 , 6 ]. For example, if ten acceleration cycles were obtained from a single tire, six of them might be used for training, two for validation, and the remaining two for testing.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, this method cannot predict values between 0%, 40%, and 80%. Li et al used a finite element (FE) tire model to elucidate the relationships among the amount of tire wear, tire loading conditions, and frequency responses of the tire [ 6 ]. The authors showed that the frequency component of the radial acceleration changes according to the amount of tire wear, internal pressure, load, and speed of the running tire.…”

Section: Related Materialsmentioning

confidence: 99%

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Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information

Kim¹,

Park²,

Kim³

2023

Sensors

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Excessive tire wear can affect vehicle driving safety. While there are various methods for predicting the tire wear amount in real-time, it is unclear which method is the most effective in terms of the difficulty of sensing and prediction accuracy. The current study aims to develop prediction algorithms of tire wear and compare their performances. A finite element tire model was developed and validated against experimental data. Parametric tire rolling simulations were conducted using various driving and tire wear conditions to obtain tire internal accelerations. Machine-learning-based algorithms for tire wear prediction utilizing various sensing options were developed, and their performances were compared. A wheel translational and rotational speed-based (V and ω) method resulted in an average prediction error of 1.2 mm. Utilizing the internal pressure and vertical load of the tire with the V and ω improved the prediction accuracy to 0.34 mm. Acceleration-based methods resulted in an average prediction error of 0.6 mm. An algorithm using both the vehicle and tire information showed the best performance with a prediction error of 0.21 mm. When accounting for sensing cost, the V and ω-based method seems to be promising option. This finding needs to be experimentally verified.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Materialsmentioning

confidence: 99%

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Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information

Kim¹,

Park²,

Kim³

2023

Sensors

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“…Accelerometer-based intelligent tires are sensitive to vibrations [23], [24]; thus, they are frequently used to estimate the roadway conditions [25], [26], [27], [28], tire force [29], [30], [31], [32], and slip angle [33], [34]. In recent years, several studies have been conducted on the estimation of tire wear [35], [36], [37] using intelligent tires. Jeong et al [35] estimated dimensionless tire dynamics parameters, which were simplified using a flexible ring tire model incorporating the acceleration of intelligent tires, and they analyzed the occurrence of tire wear.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Tire Tread Wear Detection Using Machine Learning in Real-Road Driving Conditions

et al. 2023

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The accurate detection of tire tread wear plays an important role in preventing tire-related accidents. In previous studies, tire wear detection is performed by interpreting mathematical models and tire characteristics. However, this approach may not accurately reflect the real driving environment. In this study, we propose a tire tread wear detection system that utilizes machine learning to provide accurate results under real-road driving conditions. The proposed system comprises (1) an intelligent tire that samples the measured acceleration signals and processes them in a dataset, (2) a preprocessing component that extracts features from the collected data according to the degree of wear, and (3) a detection component that uses a deep neural network to classify the degree of wear. To implement the proposed system in a vehicle, we designed an acceleration-based intelligent tire that can transmit data over wireless networks. At speeds between 30 and 80 km/h, the proposed system was experimentally demonstrated to achieve an accuracy of 95.51% for detecting tire tread wear under real-road driving conditions. Moreover, this system uses only preprocessed acceleration signals and machine-learning algorithms, without requiring complex physical models and numerical analyses.

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“…In the past few years, breakthroughs in machine learning have provided new insight for the development of robust design strategies, especially in the areas of mechanics, materials and structures [5][6][7][8]. Predictive mechanical models are Technical Editor: Monica Carvalho.…”

Section: Introductionmentioning

confidence: 99%

Rapid mechanical evaluation of the engine hood based on machine learning

Chen

Cheng

Zhang

et al. 2021

J Braz. Soc. Mech. Sci. Eng.

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With the development of machine learning and data mining, rapid design, rapid verification and rapid manufacturing have become the mainstream in the machinery industry. In this paper, the mapping function between the mechanical properties of the hood and its 11 dimensional parameters was mined using machine learning algorithms. By combining XGBoost and LightGBM algorithms with the bagging method, we proposed a hybrid model with hyperparameters optimized by the grey wolf algorithm. Subsequently, several machine learning models were trained and tested on a dataset of 6959 simulation samples, and the proposed hybrid model was found to have excellent predictive performance for the torsional stiffness ( R 2 of 0.9969 and root-mean-square error of 1.32185) and first-order modal frequency (0.9977 and 0.00989) of the hood. Moreover, the SHAP method (Shapley additive explanations) was used as a machine learning interpretation method to explain the predictive process of the mechanical performance. The results show that SHAP has great potential in model interpretation. This paper aims to develop a mathematical model of the mechanical properties of the hood, which can quickly predict the mechanical properties based on each key dimensional parameter. Therefore, engineers and designers can apply this approximate model in their design space exploration algorithms directly without training extra low-dimensional surrogate models.

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An estimation algorithm for tire wear using intelligent tire concept

Cited by 22 publications

References 12 publications

Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information

Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information

Experimental Evaluation of Tire Tread Wear Detection Using Machine Learning in Real-Road Driving Conditions

Rapid mechanical evaluation of the engine hood based on machine learning

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