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Applying Neural Networks for Tire Pressure Monitoring Systems

Alex Kost1, Wael A. Altabey2,3,4, Mohammad Noori1,2,*, Taher Awad4
Mechanical Engineering Department, California Polytechnic State University, San Luis Obispo, CA 93405, USA. 1Mechanical Engineering Department, California Polytechnic State University, San Luis Obispo, CA 93405, USA.
International Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing, China.
Nanjing Zhixing Information Technology Co., Ltd., Nanjing, China.
Mechanical Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt.
*Corresponding Author: Mohammad Noori. Email: .

Structural Durability & Health Monitoring 2019, 13(3), 247-266. https://doi.org/10.32604/sdhm.2019.07025

Abstract

A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire. A quarter-car model was developed with MATLAB and Simulink to generate simulated accelerometer output data. Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks (RNN-LSTM) and a convolutional neural network (CNN) developed in Python with Tensorflow. Bayesian Optimization via SigOpt was used to optimize training and model parameters. The predictive accuracy and training speed of the two models with various parameters are compared. Finally, future work and improvements are discussed.

Keywords

RNN-LSTM, CNN, artificial neural networks, tire pressure monitoring systems

Cite This Article

Kost, A., Altabey, W. A., Noori, M., Awad, T. (2019). Applying Neural Networks for Tire Pressure Monitoring Systems. Structural Durability & Health Monitoring, 13(3), 247–266.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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