Tire health monitoring using the intelligent tire concept

Behroozinia, Pooya; Taheri, Saied; Mirzaeifar, Reza

doi:10.1177/1475921718756602

Cited by 20 publications

(6 citation statements)

References 23 publications

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“…It is used to predict crack locations using acceleration signals obtained from a triaxial accelerometer attached to the tire inner liner. It has been observed that the radial component of the acceleration signal plays a key role in the defect detection of smart tires [4]. With the explosive development of smart wearable devices, the problems of massive energy waste and environmental pollution caused by electronic waste need to be solved urgently.…”

Section: Related Workmentioning

confidence: 99%

Construction and Evaluation of Sports Rehabilitation Training Model under Intelligent Health Monitoring

Wang

Huang

2022

Wireless Communications and Mobile Computing

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Sports rehabilitation training is a comprehensive discipline that solves and helps motor function recovery through physical exercise, also known as rehabilitation sports training. Sports rehabilitation training can help athletes restore health, treat injuries, prevent recurrence, improve physical fitness, and meet the needs of daily training. Starting from the concept of sports rehabilitation training, this paper analyzes the application of sports rehabilitation training in physical training and summarizes and analyzes the role of sports rehabilitation training. It provides reference for the application of physical rehabilitation training in sports training. Intelligent health monitoring technology has been widely used and developed, and many classic algorithms have emerged to complete the extraction of moving targets. This paper is aimed at studying the application of intelligent health monitoring to the construction of sports rehabilitation training models. It proposes the overall design of the intelligent health monitoring terminal and the common algorithms for moving target monitoring, such as optical flow method, frame difference method, and background difference method. It takes the rehabilitation training of an athlete in X city as an example to establish a model study. The final result of the experiment through sports rehabilitation training under intelligent health monitoring is that 92.6% of the athletes recovered from sports injuries through rehabilitation physical training.

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Section: Related Workmentioning

confidence: 99%

Construction and Evaluation of Sports Rehabilitation Training Model under Intelligent Health Monitoring

Wang

Huang

2022

Wireless Communications and Mobile Computing

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“…Rosu et al used the finite element tire model to predict the temperature change inside the tire until the time of takeoff of the airplane tire [ 11 ]. Behroozinia et al proposed the possibility of detecting cracks inside the tire through analysis of the acceleration signal measured inside the tire using rolling simulations of the finite element tire model [ 12 ].…”

Section: Related Materialsmentioning

confidence: 99%

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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“…Filled elastomer materials (FEM) continue to attract industrial interest due to their exceptional properties that find application in various engineering fields (Behroozinia et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

Thermo-mechanical aging of carbon-black reinforced styrene-butadiene rubber under cyclic-loading

Dinari,

Benameur,

Khoshnaw

2024

MMMS

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PurposeThe research aims to investigate the impact of thermo-mechanical aging on SBR under cyclic-loading. By conducting experimental analyses and developing a 3D finite element analysis (FEA) model, it seeks to understand chemical and physical changes during aging processes. This research provides insights into nonlinear mechanical behavior, stress softening and microstructural alterations in SBR compounds, improving material performance and guiding future strategies.Design/methodology/approachThis study combines experimental analyses, including cyclic tensile loading, attenuated total reflection (ATR), spectroscopy and energy-dispersive X-ray spectroscopy (EDS) line scans, to investigate the effects of thermo-mechanical aging (TMA) on carbon-black (CB) reinforced styrene-butadiene rubber (SBR). It employs a 3D FEA model using the Abaqus/Implicit code to comprehend the nonlinear behavior and stress softening response, offering a holistic understanding of aging processes and mechanical behavior under cyclic-loading.FindingsThis study reveals significant insights into SBR behavior during thermo-mechanical aging. Findings include surface roughness variations, chemical alterations and microstructural changes. Notably, a partial recovery of stiffness was observed as a function of CB volume fraction. The developed 3D FEA model accurately depicts nonlinear behavior, stress softening and strain fields around CB particles in unstressed states, predicting hysteresis and energy dissipation in aged SBRs.Originality/valueThis research offers novel insights by comprehensively investigating the impact of thermo-mechanical aging on CB-reinforced-SBR. The fusion of experimental techniques with FEA simulations reveals time-dependent mechanical behavior and microstructural changes in SBR materials. The model serves as a valuable tool for predicting material responses under various conditions, advancing the design and engineering of SBR-based products across industries.

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Tire health monitoring using the intelligent tire concept

Cited by 20 publications

References 23 publications

Construction and Evaluation of Sports Rehabilitation Training Model under Intelligent Health Monitoring

Construction and Evaluation of Sports Rehabilitation Training Model under Intelligent Health Monitoring

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

Thermo-mechanical aging of carbon-black reinforced styrene-butadiene rubber under cyclic-loading

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