Fractional Autoregressive Integrated Moving Average and Finite-Element Modal: The Forecast of Tire Vibration Trend | Zendy

Yujin Zhang | Zendy; Wanqing Song | Zendy; Mohammad Karimi | Zendy; Chi-Hung Chi | Zendy; Aleksey Kudreyko | Zendy

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Fractional Autoregressive Integrated Moving Average and Finite-Element Modal: The Forecast of Tire Vibration Trend

Author(s) -

Yujin Zhang,

Wanqing Song,

Mohammad Karimi,

Chi-Hung Chi,

Aleksey Kudreyko

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2855147

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper deals with the tire dynamic characteristics, which enable to forecast the trend of vibration signals, obtained from the tire-road system. Theory of the long-range dependence and the finite-element method were employed in this paper. To forecast the resonance effects, we use different frequency modes to avoid the appearance of undesirable inherent frequencies. The vibration intensity of the tire is related to the attrition rate with long-range dependence, which is distinguished by the Hurst effect. The model data can be obtained by the tire vibration sequence from a common pavement. Consequently, we used the fractional auto-regressive integrated moving average approach to forecast the vibration trend and the tire spectrum characteristics simultaneously. The proposed method can be useful for the monitoring of driving performance, tire design, and driving comfort improvement.

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