Abstract
Brakes, one of a vehicle’s most crucial safety systems, are necessary to ensure safety. They are our primary protection mechanism while driving a car on the road. A brake failure can end up causing an accident and putting lives at risk, which is why it is essential to check all its elements periodically. The car must go to service in case of brake issues: unusual noises, abnormal movements or sensations, inability to stop quickly, and warning lights. Sometimes, drivers do not associate them with brake failure and wait to take the car to check service. However, if they could determine that an issue relates to brake problems, they could immediately seek assistance. State of the art shows that expert systems and domain expertise revolutionize maintenance, reshaping diagnostics, decision-making, and predictive strategies by blending advanced AI techniques, data analysis, and real-time monitoring. On the one hand, fuzzy logic is a branch of artificial intelligence and mathematics used to model and manage uncertainty and imprecision in data and expert systems. On the other hand, Bayesian reasoning allows determining beliefs about a hypothesis based on facts. In this work, we propose developing a Fuzzy-Bayesian expert system for assisting the drivers in the maintenance of car brake systems encompassing goal setting, knowledge acquisition, interface design, and testing. Our proposal, programmed in Python, uses UPAFuzzySystems to describe fuzzy rules and Twilio to allow SMS integration in a user interface, empowering users to make informed brake system decisions from their mobile and obtain information about the status of their vehicle’s brake system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chapi-Chamorro, E.F., Fraga-Portilla, J.A., Caiza-Quispe, L.: Existing influence on the viscosity of fluids in the anti-lock braking system (ABS). Polo Conocimiento 7, 619–629 (2022)
Borawski, A., Mieczkowski, G., Szpica, D.: Composites in vehicles brake systems-selected issues and areas of development. Materials 16, 2264 (2023). https://doi.org/10.3390/MA16062264
Guerra, S.A.C., Correa, L.A.S., Maigua, D.P.P.: Eficiencia del sistema de frenos en vehículos eléctricos. Open J. Syst. (2022)
Bousdekis, A., Lepenioti, K., Apostolou, D., Mentzas, G.: A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics (Basel) 10, 828 (2021). https://doi.org/10.3390/electronics10070828
Amirkhani, A., Molaie, M.: Fuzzy controllers of antilock braking system: a review. Int. J. Fuzzy Syst. 25, 222–244 (2023). https://doi.org/10.1007/S40815-022-01376-Y/METRICS
Knaiber, M., Alawieh, L.: Bayesian inference using an adaptive neuro-fuzzy inference system. Fuzzy Sets Syst. 459, 43–66 (2023). https://doi.org/10.1016/J.FSS.2022.07.001
Naik, K.N., Patil, A.R., Patil, K.N., et al.: A python-based grade converter application. In: Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, pp. 180–184 (2023). https://doi.org/10.1109/ICEARS56392.2023.10084961
Timko, D., Rahman, M.L.: Commercial anti-smishing tools and their comparative effectiveness against modern threats. In: WiSec 2023 - Proceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks, pp. 1–12 (2023). https://doi.org/10.1145/3558482.3590173
Singh, A.: Evaluating user-friendly dashboards for driverless vehicles: evaluation of in-car infotainment in transition (2023). https://doi.org/10.25394/PGS.23750994.V1
Daniyan, I., Mpofu, K., Muvunzi, R., Uchegbu, I.D.: Implementation of artificial intelligence for maintenance operation in the rail industry. Procedia CIRP 109, 449–453 (2022). https://doi.org/10.1016/J.PROCIR.2022.05.277
Bousdekis, A., Lepenioti, K., Apostolou, D., Mentzas, G.: A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics 10, 828 (2021). https://doi.org/10.3390/ELECTRONICS10070828
Alamelu Manghai, T.M., Jegadeeshwaran, R., Sakthivel, G.: Real time condition monitoring of hydraulic brake system using naive bayes and bayes net algorithms. IOP Conf. Ser. Mater. Sci. Eng. 624, 012028 (2019). https://doi.org/10.1088/1757-899X/624/1/012028
Arena, F., Collotta, M., Luca, L., et al.: Predictive maintenance in the automotive sector: a literature review. Math. Comput. Appl. 27, 2 (2021). https://doi.org/10.3390/MCA27010002
Le, T.T., Le, M.V.: Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members. Mater. Struct./Mater. Constr. 54, 1–24 (2021). https://doi.org/10.1617/S11527-021-01646-5/METRICS
¿Qué es un sistema experto? Usos y aplicaciones en la IA. https://www.unir.net/ingenieria/revista/sistema-experto/. Accessed 24 May 2023
Tecnológica Nacional, U., Regional Rosario Autor, F., Juan Manuel, P.: Sistemas Expertos Sistemas Expertos Sistemas Expertos Sistemas Expertos (Expert System) (Expert System) (Expert System) (Expert System) Orientación I: Informática aplicada a la Ingeniería de Procesos 1 Ingeniería Química
Horvitz, E.J., Breese, J.S., Henrion, M.: Decision theory in expert systems and artificial intelligence* (1988)
Horvitz, E.J., Breese, J.S., Henrion, M.: Decision theory in expert systems and artificial intelligence. Int. J. Approximate Reasoning 2, 247–302 (1988). https://doi.org/10.1016/0888-613X(88)90120-X
Guzmán, J.J.C., Téllez, E.M., Macias, M.G.: Un software analítico de vehículos y un sonido de alerta la salvación de muchas vidas humanas. J. Sci. Res. 7, 612–633 (2022)
Avliyokulov, J.S., Pulatovich, M.S., Rakhmatov, M.I.: Main failures of the vehicle brake system, maintenance and repair. Cent. Asian J. Math. Theory Comput. Sci. 4, 63–69 (2023). https://doi.org/10.17605/OSF.IO/SMAUF
The Basics—experta unknown documentation. https://experta.readthedocs.io/en/latest/thebasics.html. Accessed 24 May 2023
Gigerenzer, G., Hoffrage, U.: How to improve Bayesian reasoning without instruction: frequency formats. Psychol. Rev. 102, 684–704 (1995). https://doi.org/10.1037/0033-295X.102.4.684
Ayal, S., Beyth-Marom, R.: The effects of mental steps and compatibility on Bayesian reasoning. Judgm. Decis. Mak. 9, 226–242 (1930). https://doi.org/10.1017/S1930297500005775
Montes Rivera, M., Olvera-Gonzalez, E., Escalante-Garcia, N.: UPAFuzzySystems: a python library for control and simulation with fuzzy inference systems. Machines 11, 572 (2023). https://doi.org/10.3390/machines11050572
Mandel, D.R.: The psychology of Bayesian reasoning (2014). https://doi.org/10.3389/fpsyg.2014.01144
Vista de SEDFE: Un Sistema Experto para el Diagnóstico Fitosanitario del Espárrago usando Redes Bayesianas. https://dspace.palermo.edu/ojs/index.php/cyt/article/view/785/687. Accessed 29 June 2023
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hernández, M.P., Montes Rivera, M., Hernández, R.P., Escobar, R.M. (2024). Brake Maintenance Diagnostic with Fuzzy-Bayesian Expert System. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H., Zatarain Cabada, R., Montes Rivera, M., Mezura-Montes, E. (eds) Advances in Computational Intelligence. MICAI 2023 International Workshops. MICAI 2023. Lecture Notes in Computer Science(), vol 14502. Springer, Cham. https://doi.org/10.1007/978-3-031-51940-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-51940-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-51939-0
Online ISBN: 978-3-031-51940-6
eBook Packages: Computer ScienceComputer Science (R0)