Journal of the Korean Society of Industry Convergence (한국산업융합학회 논문집)

The Korean Society of Industry Convergence (한국산업융합학회)
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Classification of Tire Tread Wear Using Accelerometer Signals through an Artificial Neural Network

인공신경망을 이용한 가속도 센서 기반 타이어 트레드 마모도 판별 알고리즘

  • Kim, Young-Jin (School of Mechanical Engineering, Pusan National University) ;
  • Kim, Hyeong-Jun (School of Mechanical Engineering, Pusan National University) ;
  • Han, Jun-Young (School of Mechanical Engineering, Pusan National University) ;
  • Lee, Suk (School of Mechanical Engineering, Pusan National University)
  • 김영진 (부산대학교 기계공학부) ;
  • 김형준 (부산대학교 기계공학부) ;
  • 한준영 (부산대학교 기계공학부) ;
  • 이석 (부산대학교 기계공학부)
  • Received : 2020.03.05
  • Accepted : 2020.04.01
  • Published : 2020.04.30
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Abstract

The condition of tire tread is a key parameter closely related to the driving safety of a vehicle, which affects the contact force of the tire for braking, accelerating and cornering. The major factor influencing the contact force is tread wear, and the more tire tread wears out, the higher risk of losing control of a vehicle exits. The tire tread condition is generally checked by visual inspection that can be easily forgotten. In this paper, we propose the intelligent tire (iTire) system that consists of an acceleration sensor, a wireless signal transmission unit and a tread classifier. In addition, we also presents classification algorithm that transforms the acceleration signal into the frequency domain and extracts the features of several frequency bands as inputs to an artificial neural network. The artificial neural network for classifying tire wear was designed with an Multiple Layer Perceptron (MLP) model. Experiments showed that tread wear classification accuracy was over 80%.

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References

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