Vision-based tire deformation and vehicle-bridge contact force measurement

Zhang, Cheng; Zhao, Weian; Wang, Weiguo; Zhang, Jian

doi:10.1016/j.measurement.2021.109792

Cited by 13 publications

(2 citation statements)

References 25 publications

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“…A chassis acceleration sensor can be used to measure the vertical acceleration of the vehicle. Measurement of the tire deformation is not as easy as other performance, while there are methods for finding tire deformation of the vehicle [28][29][30]. The earlier study [31] identified that the computational time costs of the control laws can be reduced by considering the LPV model.…”

Section: Simulation Resultsmentioning

confidence: 99%

An LPV-Based Online Reconfigurable Adaptive Semi-Active Suspension Control with MR Damper

et al. 2022

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This study introduces an online reconfigurable road-adaptive semi-active suspension controller that reaches the performance objectives with satisfying the dissipativity constraint. The concept of the model is based on a nonlinear static model of the semi-active Magnetorheological (MR) damper with considering the bi-viscous and hysteretic behaviors of the damper. The input saturation problem has been solved by using the proposed method in the literature that allows the integration of the saturation actuator in the initial system to create a Linear Parameter Varying (LPV) system. The control input meets the saturation constraint; therewith, the dissipativity constraint is fulfilled. The online reconfiguration and adaptivity problem is solved by using an external scheduling variable that allows the trade-off between driving comfort and road holding/stability. The control design is based on the LPV framework. The proposed adaptive semi-active suspension controller is compared to passive suspension and Bingham model with Simulink simulation, and then the adaptivity of the controller is validated with the TruckSim environment. The results show that the proposed LPV controller has better performance results than the controlled Bingham and passive semi-active suspension model.

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Section: Simulation Resultsmentioning

confidence: 99%

An LPV-Based Online Reconfigurable Adaptive Semi-Active Suspension Control with MR Damper

et al. 2022

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“…As the vibration properties (e.g., the natural frequency, amplitude, and damping ratio) of structural system vary with the mechanical parameters and boundary conditions, many researchers attempted to utilize the variation of vibration properties to detect the structural health condition [7][8][9][10]. And, the commonly used methods for vibration signal analysis included empirical mode decomposition (EMD) [11,12], short-time Fourier transform (STFT) [13,14], wavelet transform (WT) [15,16], etc.…”

Section: Introductionmentioning

confidence: 99%

Vibration-based and computer vision-aided nondestructive health condition evaluation of rail track structures

Wang

Zheng

Tang

et al. 2022

J Civil Struct Health Monit

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In railway engineering, monitoring the health condition of rail track structures is crucial to prevent abnormal vibration issues of the wheel–rail system. To address the problem of low efficiency of traditional nondestructive testing methods, this work investigates the feasibility of the computer vision-aided health condition monitoring approach for track structures based on vibration signals. The proposed method eliminates the tedious and complicated data pre-processing including signal mapping and noise reduction, which can achieve robust signal description using numerous redundant features. First, the method converts the raw wheel–rail vibration signals directly into two-dimensional grayscale images, followed by image feature extraction using the FAST-Unoriented-SIFT algorithm. Subsequently, Visual Bag-of-Words (VBoW) model is established based on the image features, where the optimal parameter selection analysis is implemented based on fourfold cross-validation by considering both recognition accuracy and stability. Finally, the Euclidean distance between word frequency vectors of testing set and the codebook vectors of training set is compared to recognize the health condition of track structures. For the three health conditions of track structures analyzed in this paper, the overall recognition rate could reach 96.7%. The results demonstrate that the proposed method performs higher recognition accuracy and lower bias with strong time-varying and random vibration signals, which has promising application prospect in early-stage structural defect detection.

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Calculation and Correction Method of Dynamic Tire Loads in Accelerating and Braking Conditions

Wang,

Zhang,

Xie

et al. 2024

Int.J Automot. Technol.

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Vision-based tire deformation and vehicle-bridge contact force measurement

Cited by 13 publications

References 25 publications

An LPV-Based Online Reconfigurable Adaptive Semi-Active Suspension Control with MR Damper

An LPV-Based Online Reconfigurable Adaptive Semi-Active Suspension Control with MR Damper

Vibration-based and computer vision-aided nondestructive health condition evaluation of rail track structures

Calculation and Correction Method of Dynamic Tire Loads in Accelerating and Braking Conditions

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