Applying Neural Networks for Tire Pressure Monitoring Systems

• 作者: Alex Kost Mohammad Noori Taher Awad Wael A.Altabey
• 来源期刊: 结构耐久性与健康监测(英文)
• 学科: 工学
• 关键词: RNN-LSTM CNN artificial NEURAL NETWORKS TIRE pressure monitoring systems
• 年，卷（期）: 2019,（3）
• 页码范围: 247-266
• 页数: 20页
• 分类号: TP3

摘要

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.