Uma Abordagem Interativa para Auxiliar o Diagnóstico Automotivo

  • Leonardo Presoto de Oliveira UTFPR
  • Marco Aurélio Wehrmeister UTFPR
  • André Schneider de Oliveira UTFPR

Resumo

Este trabalho tem como objetivo propor uma abordagem para auxiliar o diagnóstico de defeitos nos automóveis, relacionando os dados obtidos através da telemetria do veículo como as percepções do motorista sobre uma determinada falha. A inclusão do motorista no processo de diagnóstico permite que os engenheiros identifiquem elementos que podem ser melhorados no carro, mesmo que eles não apresentem erro aparente. A opinião do motorista deve ser considerada, uma vez que ele/ela é incluído no processo como um novo “sensor” (o mais inteligente e importante de todos) capaz de reportar suas percepções. Neste sentido, este trabalho contribui com: (i) a busca por alternativas para aplicar de maneira eficiente a conectividade dos veículos no processo de diagnóstico; (ii) permitir que as montadoras obtenham informações mais concretas dos veículos que comercializam. Para tanto propõe-se um abordagem que integra os dados fornecidos pelo motorista com os do carro, permitindo que sejam realizados diagnósticos preventivos mais completos do que aqueles baseados apenas uma telemetria. Para tanto, o motorista fornece comandos por texto ou voz e um software contido no celular solicita ao OBD os dados de telemetria necessários para obter as informações desejadas. A abordagem proposta foi avaliada através de um experimento no qual analistas de diagnóstico responderam a um questionário que buscava evidenciar que a abordagem proposta influencia no processo de diagnóstico, fazendo com que a solução do problema seja encontrada em menos etapas em comparação com o processo atual.

Referências

STARON, M. Automotive Software Architectures: An Introduction. Springer, 2017.

LAWRENZ, W. CAN system engineering. From theory to practical applications, New York, 1997.

GODAVARTY, S.; BROYLES, S.; PARTEN, M. Interfacing to the onboard diagnostic system. In: Vehicular Technology Conference, 2000. IEEE-VTS Fall VTC 2000. 52nd. IEEE, 2000.

ISERMANN, R. Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer Science&Business Media, 2006.

Vector Informatik GmbH. Introduction to Vehicle Diagnostics. 2016.

Gill, N.; Winkler, M. Cars Online 2014 - Generation Connected. Capgemini Study. 2014. https://www.capgemini.com/wpcontent/uploads/2017/07/cars_online_2014_final_web_group_1.pdf

DE OLIVEIRA, Leonardo Presoto; WEHRMEISTER, Marco Aurélio; DE OLIVEIRA, André Schneider. Systematic Literature Review on Automotive Diagnostics. In: Computing Systems Engineering (SBESC), 2017 VII Brazilian Symposium on. IEEE, 2017. p. 1-8.

GAO, Ai Lin; WU, Yan Xiang. A design of voice control car base on spce061a single chip. In: Electronics, Computer and Applications, 2014 IEEE Workshop on. IEEE, 2014. p. 214-217.

CHEN, S; CHEN, J; LU, K. The use of cloud speech recognition technology in vehicle diagnosis applications. In: Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2011 Fifth International Conference on. IEEE, 2011. p. 567-570.

VARRIER, S.; KOENIG, D.; MARTINEZ, J. J. Robust fault detection for vehicle lateral dynamics. In: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on. IEEE, 2012. p. 4366-4371.

TANG, K. Z. et al. Development of a remote telemetry and diagnostic system for electric vehicles and electric vehicle supply equipment. In: Control and Automation (ICCA), 2013 10th IEEE International Conference on. IEEE, 2013. p. 609-613.

HÄNDEL, Peter et al. Smartphone-based measurement systems for road vehicle traffic monitoring and usage-based insurance. IEEE Systems Journal, v. 8, n. 4, p. 1238-1248, 2014.

POSTOLACHE, Mihai; NEAMTU, Gabriel; TROFIN, Sorin Dumitru. CAN-Ethernet gateway for automotive applications. In: System Theory, Control and Computing (ICSTCC), 2013 17th International Conference. IEEE, 2013. p. 422-427.

YUN, Doo Seop et al. A study on the vehicular wireless base-station for in-vehicle wireless sensor network system. In: Information and Communication Technology Convergence (ICTC), 2014 International Conference on. IEEE, 2014. p. 609-610.

ZHOU, Y.; ZHANG, Y.. Applications of Bayesian Network in Fault Diagnosis of Braking Deviation System. In: Int. Symp. on Computational Intelligence and Design (ISCID). 2011. p. 170-173.

SURESH, Vaishnavi; NIRMALRANI, V. Android based vehicle diagnostics and early fault estimation system. In: Computation of Power, Energy, Information and Communication (ICCPEIC), 2014 International Conference on. IEEE, 2014. p. 417-421.

WANG, Zhao et al. Design of an arduino-based smart car. In: SoC Design Conference (ISOCC), 2014 International. 2014. p. 175-176.

SALUNKE,A.A., JAKHETE, M.D. Designing and Modeling of Distant Words Recognition Pattern System for the Motion Control Systems in Vehicles. Journal of Science and Research, IJSR. v.4, 2015.

PALLADINO, A.; FIENGO, G.; LANZO, D. A portable hardware-inthe-loop (HIL) device for automotive diagnostic control systems. ISA transactions, v. 51, n. 1, p. 229-236, 2012.

SZYMAŃSKI, G. M. et al. Application of time–frequency analysis to the evaluation of the condition of car suspension. Mechanical Systems and Signal Processing, v. 58, p. 298-307, 2015.

LU, Yi; CHEN, Tie Qi; HAMILTON, Brennan. A fuzzy diagnostic model and its application in automotive engineering diagnosis. Applied Intelligence, v. 9, n. 3, p. 231-243, 1998.

BARONE, Stefano; D'AMBROSIO, Paolo; ERTO, Pasquale. A statistical monitoring approach for automotive on-board diagnostic systems. Quality and Reliability Engineering International, v. 23, n. 5, p. 565-575, 2007.

KRÜGER, I. et al. Improving the development process for automotive diagnostics. In: Proceedings of the International Conference on Software and System Process. IEEE Press, 2012. p. 63-67.

JING, Yuxin et al. AndroRC: An Android remote control car unit for search missions. In: Systems, Applications and Technology Conference (LISAT), 2014 IEEE Long Island. IEEE, 2014. p. 1-5.

YOON, Jae-Hwan et al. Communication architecture and application for vehicle to nomadic devices communication. In: Information and Communication Technology Convergence (ICTC), 2014 International Conference on. IEEE, 2014. p. 681-682.

RUSSELL, Stuart J.; NORVIG, Peter. Artificial intelligence: a modern approach (International Edition). 2002.

WITTEN, Ian H. et al. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.

HAN, Jiawei; PEI, Jian; KAMBER, Micheline. Data mining: concepts and techniques. Elsevier, 2011.

CHO, Y.H.; KIM, J.K.; KIM, S.H.. A personalized recommender system based on web usage mining and decision tree induction. Expert systems with Applications, v. 23, n. 3, p. 329-342, 2002.

LUHN, Hans Peter. The automatic creation of literature abstracts. IBM Journal of research and development, v. 2, n. 2, p. 159-165, 1958.

SILLA JR, Carlos N.; KAESTNER, Celso AA. Estudo de métodos automáticos para sumarizaçao de textos. Simpósio de Tecnologias de Documentos, p. 45-49, 2002.

P. Pires. Obd-ii java api. https://github.com/pires/obd-java-api. Acessado: 27-07-2018

SUGAYAMA, Ricardo; NEGRELLI, Evaldir. Veículo conectado na rota da indústria 4.0. Blucher Engineering Proceedings, v. 3, n. 1, p. 48-63, 2016.
Publicado
2018-11-06
Como Citar
DE OLIVEIRA, Leonardo Presoto; WEHRMEISTER, Marco Aurélio; DE OLIVEIRA, André Schneider. Uma Abordagem Interativa para Auxiliar o Diagnóstico Automotivo. Anais Estendidos do Simpósio Brasileiro de Engenharia de Sistemas Computacionais (SBESC), [S.l.], nov. 2018. ISSN 2763-9002. Disponível em: <https://sol.sbc.org.br/index.php/sbesc_estendido/article/view/11007>. Acesso em: 18 maio 2024.