Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review
DOI:
https://doi.org/10.21015/vtse.v12i1.1713Abstract
The automobile industry has a growing need for reliable and safe health monitoring systems equipped with low-cost sensor networks and intelligent algorithms. This paper provides an overview of approaches already exist, used in on-board health monitoring systems for vehicles. It focuses on the methodologies, theories, and applications employed in the data measurement and data analysis systems of vehicle (cars) on-board health monitoring systems. A fault detection and diagnosis system, which is accurate, plays a vital role in ensuring the safety of autonomous vehicles by preventing potentially dangerous situations. This study focuses on emphasizing a fault diagnosis system that utilizes hybrid methods. Among the various options considered in this analysis, internal sensors emerge as the preferred choice due to their numerous benefits, including affordability, durability, widespread availability, ease of access, and low energy consumption. Model-based methods require various techniques that may introduce errors to estimation results, while signal-based methods necessitate a time-consuming process of including all possible conditions in a pre-built database. Based on this review, future development trends in designing new low-cost health monitoring systems for vehicles are also discussed.
References
OBD-II, Available: http://www.obdii.com
Y. Fang, H. Min, W. Wang, Z. Xu, and X. Zhao, "A Fault Detection and Diagnosis System for Autonomous Vehicles Based on Hybrid Approaches," *IEEE Sensors Journal*, pp. 1–1, 2020, doi: 10.1109/jsen.2020.2987841.
Z. Gao, C. Cecati, and S. X. Ding, "A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches," *IEEE Transactions on Industrial Electronics*, vol. 62, no. 6, pp. 3768–3774, Jun. 2015.
S. Yin, S. Ding, X. Xie, and H. Luo, "A review on basic data-driven approaches for industrial process monitoring," *IEEE Trans. Ind. Electron.*, vol. 61, no. 11, pp. 6418–6428, Nov. 2014.
N. Meskin and K. Khorasani, *Fault Detection and Isolation: Multi-Vehicle Unmanned Systems*. New York, USA: Springer, 2011.
J. V. Moniaga et al., "Diagnostics vehicle’s condition using OBD-ii and raspberry pi technology: study literature," *J. Phys. Conf. Series*, vol. 978, p. 012011, 2018.
S. Vneeth, K. P. Prithvil, N. R. Sri Hari, R. Thushar, and M. Rajeswari, "OnBoard Diagnostics and Driver Profiling," in *2020 5th International Conference on Computing, Communication and Security (ICCCS)*, 2020, doi: 10.1109/icccs49678.2020.9277449.
D. Rimpas, A. Papadakis, and M. Samarakou, "OBD-II Sensor Diagnostics for Monitoring Vehicle Operation and Consumption," *Energy Reports*, 2019.
U. Shafi et al., "Vehicle Remote Health Monitoring and Prognostic Maintenance System," *Journal of Advanced Transportation*, 2018, doi: 10.1155/2018/8061514.
A. X. A. Sim and B. Sitohang, "OBD-II Standard Car Engine Diagnostic Software Development," in *2014 International Conference on Data and Software Engineering (ICODSE 2014)*, 2014, doi: 10.1109/ICODSE.2014.7062704.
S. Vasavi, K. Aswarth, T. S. D. Pavan, and A. A. Gokhale, "Predictive Analytics as a Service for Vehicle Health Monitoring Using Edge Computing and AK-NN Algorithm," *Materials Today: Proceedings*, vol. 46, pp. 8645–54, 2021, doi: 10.1016/j.matpr.2021.03.658.
M. M. Oluwaseyi and A. M. Sunday, "Specifications and Analysis of Digitized Diagnostics of Automobiles: A Case Study of on Board Diagnostic (OBD II)," *International Journal of Engineering Research and Technology (IJERT)*, vol. 09, no. 01, pp. 01–05, Jan. 2020, doi: 10.17577/IJERTV9IS010045.
B. Bánhelyi and T. Szabó, "Data Mining and Analysis for Data From Vehicles Based on the OBDII Standard," in *11th International Conference on Applied Informatics*, Eger, Hungary, Jan. 29-31, 2020. [Online]. Available: http://ceur-ws.org
X. Cai, C. Lei, B. Peng, X. Tang, and Z. Gao, "Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data," *Journal of Advanced Transportation*, vol. 2020, pp. 1–13, 2020, doi 10.1155/2020/3024101.
Geivanidis et al., "Next generation of vehicle diagnostics based on advanced onboard monitoring and cloud-based diagnostics," in *8th Transport Research Arena TRA 2020*, April 27-30, 2020, Helsinki, Finland.
X. Zheng, S. Lu, L. Yang, M. Yan, G. Xu, X. Wu, Y. Wu, "Real-world fuel consumption of light-duty passenger vehicles using on-board diagnostic cimplementation of hybrid artificial intelligence model," *Eng Comput*, vol. 36, no. 1, pp. 1–11, 2020.
A. Kulakov, E. Muhametdinov, E. Tsybunov, A. Badriev, "Development of on-board Diagnostic Systems for Vehicles to Improve their Reliability," *MATEC Web of Conferences*, vol. 334, p. 01037, 2021, doi: 10.1051/matec-conf/202133401037.
L. Wang et al., "Design of OBD function test on production vehicle (PVE)," in *2020 6 th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2020)*, vol. 268, 2021, p. 01047, doi: 10.1051/e3sconf/202126801047.
B. Suwandi, W. S. Pinastiko, and R. Roestam, "OBD-II Sensor Approaches for The IMU and GPS Based Apron Vehicle Positioning System," in *2019 International Conference on Sustainable Engineering and Creative Computing (ICSECC)*, 2019, doi: 10.1109/icsecc.2019.8907036.
N. Kushiro, Y. Oniduka, and Y. Sakurai, "Initial practice of telematics-based prognostics for commercial vehicles," *Procedia Comput Sci*, vol. 112, pp. 2155–64, 2017.
D. Sik, T. Balogh, P. Ekler, and L. Lengyel, "Comparing OBD and CAN Sampling on the go with the SensorHUB Framework," *Procedia Eng*, vol. 168, pp. 39–42, 2016.
M. D’Agostino, M. Naddeo, and G. Rizzo, "Development and validation of a model to detect active gear via OBD data for a Through-The-Road Hybrid Electric Vehicle," *IFAC Proc Vol*, vol. 47, no. 3, pp. 6618–23, 2014.
T. Zahariadis et al., "FIWARE lab: managing resources and services in a cloud federation supporting future internet applications," in *7th International Conference on Utility and Cloud Computing*, IEEE/ACM, 2014.
Wei, Lei, Haitao Duan, Dan Jia, Yongliang Jin, Song Chen, Lian Liu, Jianfang Liu, Xianming Sun, and Jian Li, "Motor Oil Condition Evaluation Based on On-Board Diagnostic System," *Friction*, vol. 8, no. 1, pp. 95–106, 2020, doi: 10.1007/s40544-018-0248-0.
Y. He and L. Feng, "Diesel fuel injection system faults diagnosis based on fuzzy injection pressure pattern recognition," 2004.
A. Schilling, "Model-based detection and isolation of faults in the air and fuel paths of common-rail DI deisel engines equiped with a lambda and a nitrogen oxides sensor," PhD thesis, ETH Zurich, Zurich, Switzerland, 2008.
A. Schilling, A. Amstutz, and L. Guzzella, "Model based detection and isolation of faults due to ageing in the air and fuel paths of common rail direct injection diesel engines equipped with a and a nitrogen oxides sensor," *Proceedings of the Institution of Mechanical Engineering, Part D: Journal of Automobile Engineering*, vol. 222, no. 1, pp. 101–117, 2008.
A. A. Al-Musawi, A. A. H. Alwanas, S. Q. Salih et al., "Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model," *Eng Comput*, vol. 36, no. 1, pp. 1–11, 2020.
R. Ferrari, C. Mancini-Terracciano, C. Voena et al., "MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer," *Eur J Radiol*, vol. 118, pp. 1–9, 2019.
T. Fu and C. Wang, "A hybrid wind speed forecasting method and wind energy resource analysis based on a swarm intelligence optimization algorithm and an artificial intelligence model," *Sustainability*, vol. 10, no. 11, p. 3913, 2018.
X. Sun, J. Young, J. H. Liu et al., "Prediction of pork loin quality using online computer vision system and artificial intelligence model," *Meat Sci*, vol. 140, pp. 72–77, 2018.
S.-C. Kin, Y.-T. Chen, Y.-T. Shih, and S.-H. Chen, "A study on remote on-line diagnostic system for vehicles by integrating the technology of OBBD, GPS, and 3G," *World Academy of Science, Engineering and Technology*, vol. 56, 2009.
B. Lee, "OBD-II(exhaust)," Kyung Young sa, 2005.
"On-Board Diagnostics," http://en.wikipedia.org/wiki/OBD-II#OBD-II.
A. I. Santini, *OBD-II: Function, Monitors and Diagnostic Techniques*, Cengage Learning, 2010.
ISO 14230-4, *Road Vehicles-Diagnostic Systems*, https://law.resource.org/pub/us/cfr/ibr/004/iso.14230-4.2000.pdf.
J. Bae, "Design and Development of Automobile Fault Diagnosis System Using AI Machine Learning," in *Next-generation convergence information service technology society*, vol. 2, 2018.
R. Tafreshi, H. Ahmadi, F. Sassani, et al., "Informative wavelet algorithm in diesel engine diagnosis," in *Proceedings of the IEEE International Symposium on Intelligent Control*, IEEE, 2002, pp. 361-366.
J.-D. Wu and C.-H. Liu, "Investigation of engine fault diagnosis using discrete wavelet transform and neural network," *Expert Syst Appl*, vol. 35, no. 3, pp. 1200-1213, 2008.
J.-D. Wu and C.-H. Liu, "An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network," *Expert Syst Appl*, vol. 36, no. 3, pp. 4278-4286, 2009.
D. V. Prokhorov, "Toyota prius HEV neurocontrol and diagnostics," *Neural Netw*, vol. 21, no. 2, pp. 458-465, 2008.
U. Shafi, A. Safi, A. R. Shahid, S. Ziauddin, and M. Q. Saleem, "Vehicle remote health monitoring and prognostic maintenance system," *J Adv Trans*, vol. 201, pp. 1–10, 2018.
N. Gupta, M. Khosravy, S. Gupta, N. Dey, and R. G. Crespo, "Lightweight artificial intelligence technology for health diagnosis of agriculture vehicles: parallel evolving artificial neural networks by genetic algorithm," *Int J Parallel Program*, pp. 1–26, 2020.
Liu et al., "Fault mode detection of a hybrid electric vehicle by using support vector machine," in *2022 The 3rd International Conference on Power Engineering (ICPE 2022)*, Science and Engineering Institute, China Chapter, December 09–11, Sanya, Hainan Province, China, 2022, doi: 10.1016/j.egyr.2023.04.328.
J. Buddhakulsomsiri and A. Zakarian, "Sequential pattern mining algorithm for automotive warranty data," *Computers Industrial Engineering*, vol. 57, no. 1, pp. 137–147, 2009.
A. K. Choudhary, J. A. Harding, and M. K. Tiwari, "Data mining in manufacturing: a review based on the kind of knowledge," *Journal of Intelligent Manufacturing*, vol. 20, no. 5, pp. 501–521, 2009.
S. Byttner, T. Rögnvaldsson, and M. Svensson, "Consensus self-organized models for fault detection (COSMO)," *Engineering Applications of Artificial Intelligence*, vol. 24, no. 5, pp. 833–839, 2011.
R. Prytz, S. Nowaczyk, and S. Byttner, "Towards relation discovery for diagnostics," in *Proceedings of the 1st International Workshop on Data Mining for Service and Maintenance, KDD4Service 2011—Held in Conjunction with SIGKDD’11*, pp.23–27, USA, August 2011.
A. Alzghoul, M. Löfstrand, and B. Backe, "Data stream forecasting for system fault prediction," *Computers Industrial Engineering*, vol. 62, no. 4, pp. 972–978, 2012.
K. Napierala and J. Stefanowski, "BRACID: a comprehensive approach to learning rules from imbalanced data," *Journal of Intelligent Information Systems*, vol. 39, no. 2, pp. 335–373, 2012.
R. Prytz, S. Nowaczyk, T. Rögnvaldsson, and S. Byttner, "Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data," *Engineering Applications of Artificial Intelligence*, vol. 41, pp. 139–150, 2015.
M. P. Rajakumar, J. Ramya, and B. U. Maheswari, "Health monitoring and fault prediction using a lightweight deep convolutional neural network optimized by Levy flight optimization algorithm," *Neural Computing and Applications*, 2021, doi: 10.1007/s00521-021-05892-0.
K. Khorsravinia, M. K. Hassan, R. Z. A. Rahman, and S. A. R. Al-Haddad, "Integrated OBD-II and mobile application for electric vehicle (EV) monitoring system," in *2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS)*, 2017, doi: 10.1109/i2cacis.2017.8239058.
B. Bánhelyi and T. Szabó, "Data mining and analysis for data from vehicles based on the OBDII standard," *CEUR Workshop Proceedings*, vol. 2650, pp. 30–37, 2020.
K. Khorsravinia et al., "Integrated OBD-II and mobile application for electric vehicle (EV) monitoring system," in *Proceedings - 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems, I2CACIS 2017*, 2017, pp. 202–206. doi: 10.1109/I2CACIS.2017.8239058.
B. Kowalik, "Introduction to car failure detection system based on diagnostic interface," in *2018 International Interdisciplinary PhD Workshop, IIPhDW 2018*, 2018, pp. 4–7. doi: 10.1109/IIPHDW.2018.8388233.
H. Liu, X. Song, and F. Zhang, "Fault diagnosis of new energy vehicles based on improved machine learning," *Soft Computing*, vol. 25, no. 18, pp. 12091–12106, 2021. doi: 10.1007/s00500-021-05860-9.
F. R. von Glehn et al., "Telematics and machine learning system for estimating the load condition of a heavy-duty vehicle," in *5th International Conference on Industry 4.0 and Smart Manufacturing*, 2024, pp. 2616–2625, *Procedia Computer Science*, vol. 232.
B. Šarkana, V. Harantováa, and M. Loman, "The effect of emergency engine mode on the fuel consumption of a passenger car," in *TRANSCOM 2023: 15th International Scientific Conference on Sustainable, Modern and Safe Transport*, 2023, pp. 434–441, *Transportation Research Procedia*, vol. 74.
R. Vermeulena et al., "Tampering of environmental protection systems on vehicles: Status quo and perspectives," in *Transport Research Arena (TRA) Conference*, 2023, pp. 2824–2831, *Transportation Research Procedia*, vol. 72.
M. O. Rawashdeh et al., "Intelligent Automobiles Diagnostic System," *International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING*, ISSN: 2147-6799, 2023.
J. N. Juwono et al., "Motorized Vehicle Diagnosis Design Using the Internet of Things Concept with the Help of Tsukamoto’s Fuzzy Logic Algorithm," *Journal of Robotics and Control (JRC)*, vol. 4, no. 2, March 2023, ISSN: 2715-5072, DOI: 10.18196/jrc.v4i2.17256.
T. Bohne, A.-K. P. Windler, and M. Atzmueller, "A Neuro-Symbolic Approach for Anomaly Detection and Complex Fault Diagnosis Exemplified in the Automotive Domain," in *K-CAP ’23*, December 05–07, 2023, Pensacola, FL, USA. ACM, ISBN 979-8-4007-0141-2/23/12, DOI: 10.1145/3587259.3627546.
S. K. Singh and A. K. Singh, "A study: OBD III standard and its predecessors OBD II and OBD I," 2021.
A. E. Kizito, E. Ojei, and M. D. Okpor, "A Fuzzy Logic-Based Automobile Fault Detection System Using Mamdani Algorithm," *International Journal of Scientific Research and Management (IJSRM)*, vol. 12, no. 03, pp. 1081-1093, 2024, DOI: 10.18535/ijsrm/v12i03.ec06.
O. Arafa et al., "A Novel Electronics Control Unit for Early Detection of Vehicle," *Research Square*, 2023. https://doi.org/10.21203/rs.3.rs-3523552/v1.
M. K. Thomas and S. Sumathi, "Design of software-oriented technician for vehicle’s fault system prediction using AdaBoost and random forest classifiers," *International Journal of Engineering, Science and Technology*, vol. 14, no. 1, pp. 28-51, 2022.
T. Wang et al., "Remote supervision strategy based on in-use vehicle OBD data flow," in *VESEP2020*, *E3S Web of Conferences*, vol. 268, 01007, 2021, DOI: 10.1051/e3sconf/202126801007.
X. Yang, "Framework of Electric Vehicle Fault Diagnosis System Based on Diagnostic Communication," *International Journal of Engineering, IJE TRANSACTIONS A: Basics*, vol. 37, no. 01, pp. 1194-1207, 2024, doi: 10.5829/ije.2024.37.06c.16
Certainly! Here are the references formatted in IEEE style:
Z. Ruiwen et al., "Research on a Comprehensive Evaluation System for Driving Behavior by Introducing Fuzzy Evaluation Based on Natural Driving Data," *Medicon Engineering Themes*, vol. 5, no. 1, pp. 146-153, July 2023, doi: 10.55162/MCET.05.146.
L. Heroual, M. L. Berkane, and N. E. H. Bouzerzour, "Towards an AI-Based Approach for Adaptive Emission Control and Sensor Diagnostics: A gasoline engine case study," in *TACC 2023: Tunisian-Algerian Joint Conference on Applied Computing*, November 06–08, 2023, Sousse, Tunisia.
S. K. Singh, A. K. Singh, and A. Sharma, "OBD - II based Intelligent Vehicular Diagnostic System using IoT," in *ISIC’21: International Semantic Intelligence Conference*, February 25-27, 2021, Delhi, India.
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