Fault Diagnosis of Vehicle Tire Pressure Using Bayesian Networks with Real-Time Robot Operating Systems (ROS) Applications †
Abstract
1. Introduction
2. Fault Diagnosis Structure
2.1. Sensors
2.2. Calculatıon of Tire Pressure Residuals
2.3. Fault Diagnosis Algorithm
2.4. Dynamic Bayesian Network
2.5. ROS Structure
3. Test Scenario
3.1. Test Scenario
Right Front Tire Pressure Fault Scenario
4. Results
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Symbol | Description |
---|---|
Front-left wheel speed | |
Front-right wheel speed | |
Rear-left wheel speed | |
Rear-right wheel speed | |
Steering wheel angle | |
Yaw rate | |
Front-left wheel pressure | |
Front-right wheel pressure | |
Rear-left wheel pressure | |
Rear-right wheel pressure |
Fault | Description |
---|---|
Front-right tire fault | |
Front-left tire fault | |
Rear-right tire fault | |
Rear-left tire fault | |
Front-right wheel speed fault | |
Front-left wheel speed fault | |
Rear-right wheel speed fault | |
Rear-left wheel speed fault | |
Yaw rate sensor fault | |
Steering wheel angle sensor fault |
Faults | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | |
1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | |
1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | |
1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | |
1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | |
1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | |
1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | |
1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | |
1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | |
1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Tire | Tire Pressure (PSI) | Tire Fault Value |
---|---|---|
Left-front tire | 2.1 | 0 |
Right-front tire | 1.8 | 1 |
Left-rear tire | 2.1 | 0 |
Right-rear tire | 2.2 | 0 |
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Share and Cite
Bodrumlu, T.; Gozum, M.; Kavak, B. Fault Diagnosis of Vehicle Tire Pressure Using Bayesian Networks with Real-Time Robot Operating Systems (ROS) Applications. Eng. Proc. 2024, 82, 17. https://doi.org/10.3390/ecsa-11-20438
Bodrumlu T, Gozum M, Kavak B. Fault Diagnosis of Vehicle Tire Pressure Using Bayesian Networks with Real-Time Robot Operating Systems (ROS) Applications. Engineering Proceedings. 2024; 82(1):17. https://doi.org/10.3390/ecsa-11-20438
Chicago/Turabian StyleBodrumlu, Tolga, Murat Gozum, and Batıkan Kavak. 2024. "Fault Diagnosis of Vehicle Tire Pressure Using Bayesian Networks with Real-Time Robot Operating Systems (ROS) Applications" Engineering Proceedings 82, no. 1: 17. https://doi.org/10.3390/ecsa-11-20438
APA StyleBodrumlu, T., Gozum, M., & Kavak, B. (2024). Fault Diagnosis of Vehicle Tire Pressure Using Bayesian Networks with Real-Time Robot Operating Systems (ROS) Applications. Engineering Proceedings, 82(1), 17. https://doi.org/10.3390/ecsa-11-20438