A proof-of-concept indirect tire-pressure monitoring system is developed using neural net- works 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.
","abstract_html":"<p>A proof-of-concept indirect tire-pressure monitoring system is developed using neural net- works 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.</p>","abstract_has_math":false,"creators":["Kost, Alex"],"institution":null,"degree_name":"MS in Mechanical Engineering","degree_level":null,"degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Mohammad Noori"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-01T08:00:00Z","date_published":"2018-03-01T08:00:00Z","updated_at":"2026-07-24T01:31:56Z","subjects":["Neural Networks","ANN","CNN","RNN","TPMS","Tire Pressure Monitoring System","Computational Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2018.5"],"render_values":[{"text":"10.15368/theses.2018.5","href":"https://doi.org/10.15368/theses.2018.5","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1827","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mohammad Noori"]},{"key":"dc:creator","label":"Author","values":["Kost, Alex"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-03-18T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Mechanical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neural Networks","ANN","CNN","RNN","TPMS","Tire Pressure Monitoring System","Computational Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1827","10.15368/theses.2018.5"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A proof-of-concept indirect tire-pressure monitoring system is developed using neural net- works 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.
"]},{"key":"dc:title","label":"Title","values":["Applying Neural Networks for Tire Pressure Monitoring Systems"]}]}],"canonical_facts":{"dc:contributor":["Mohammad Noori"],"dc:creator":["Kost, Alex"],"dc:date.available":["2018-03-18T07:00:00Z"],"dc:description.abstract":["A proof-of-concept indirect tire-pressure monitoring system is developed using neural net- works 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.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1827","10.15368/theses.2018.5"],"dc:subject":["Neural Networks","ANN","CNN","RNN","TPMS","Tire Pressure Monitoring System","Computational Engineering"],"dc:title":["Applying Neural Networks for Tire Pressure Monitoring Systems"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_name":["MS in Mechanical Engineering"]},"updated_at":"2026-07-24T01:31:56Z"}