Artificial intelligence and machine learning-based diagnosis and prediction of vehicle failures: a review of modern approaches
Keywords:
vehicles, artificial intelligence, machine learning, technical diagnostics, fault detection, failure prediction, predictive maintenance, operational reliability, CAN-bus, OBD, neural networks, remaining useful lifeAbstract
This paper reviews modern approaches to the application of artificial intelligence and machine learning technologies for monitoring vehicle technical condition, early fault detection, and failure prediction. Particular attention is given to the role of intelligent diagnostic systems in improving vehicle operational reliability, reducing maintenance costs, and preventing unexpected failures. The review summarizes the application of supervised and unsupervised machine learning algorithms, neural networks, deep learning techniques, ensemble models, and predictive maintenance methods in vehicle diagnostics. The possibilities of processing operational data obtained from CAN-bus, OBD systems, telematics platforms, and various onboard sensors for fault classification, technical condition assessment, and remaining useful life estimation are also examined. Based on the reviewed approaches, the main advantages and current limitations of artificial intelligence-driven vehicle diagnostics are identified, and promising directions for further development of intelligent fault diagnosis and prognostics systems in vehicle operation are outlined
References
Jardine A. K. S., Lin D., Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance // *Mechanical Systems and Signal Processing*. – 2006. – Vol. 20, No. 7. – P. 1483–1510. – DOI: 10.1016/j.ymssp.2005.09.012.
Zhao R., Yan R., Chen Z., Mao K., Wang P., Gao R. X. Deep learning and its applications to machine health monitoring // *Mechanical Systems and Signal Processing*. – 2019. – Vol. 115. – P. 213–237. – DOI: 10.1016/j.ymssp.2018.05.050
Lei Y., Yang B., Jiang X., Jia F., Li N., Nandi A. K. Applications of machine learning to machine fault diagnosis: A review and roadmap // *Mechanical Systems and Signal Processing*. – 2020. – Vol. 138. – Art. 106587. – DOI: 10.1016/j.ymssp.2019.106587
Theissler A., Pérez-Velázquez J., Kettelgerdes M., Elger G. Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry // *Reliability Engineering & System Safety*. – 2021. – Vol. 215. – Art. 107864. – DOI: 10.1016/j.ress.2021.107864.
Jain M., Vasdev D., Pal K., Sharma V. Systematic literature review on predictive maintenance of vehicles and diagnosis of vehicle’s health using machine learning techniques // *Computational Intelligence*. – 2022. – Vol. 38, No. 6. – P. 1990–2008. – DOI: 10.1111/coin.12553.
Shafi U., Safi A., Shahid A. R., Ziauddin S., Saleem M. Q. Vehicle Remote Health Monitoring and Prognostic Maintenance System // *Journal of Advanced Transportation*. – 2018. – Vol. 2018. – Art. ID 8061514. – 10 p. – DOI: 10.1155/2018/8061514.
Li X., Wang N., Lyu Y., Duan Y., Zhao J. Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification // *Electronics*. – 2023. – Vol. 12, No. 3. – Art. 511. – DOI: 10.3390/electronics12030511.
Cervantes-Bobadilla M., García-Morales J., Saavedra-Benítez Y. I., Hernández-Pérez J. A., Adam-Medina M., Guerrero-Ramírez G. V., Escobar-Jiménez R. F. Multiple fault detection and isolation using artificial neural networks in sensors of an internal combustion engine // *Engineering Applications of Artificial Intelligence*. – 2023. – Vol. 117, Part A. – Art. 105524. – DOI: 10.1016/j.engappai.2022.105524.
Rahim M. A., Rahman M. M., Islam M. S., Muzahid A. J. M., Rahman M. A., Ramasamy D. Deep learning-based vehicular engine health monitoring system utilising a hybrid convolutional neural network/bidirectional gated recurrent unit // *Expert Systems with Applications*. – 2024. – Vol. 257. – Art. 125080. – DOI: 10.1016/j.eswa.2024.125080.
Rauf H., Khalid M., Arshad N. Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling // *Renewable and Sustainable Energy Reviews*. – 2022. – Vol. 156. – Art. 111903. – DOI: 10.1016/j.rser.2021.111903.
Renold A. P., Kathayat N. S. Comprehensive Review of Machine Learning, Deep Learning, and Digital Twin Data-Driven Approaches in Battery Health Prediction of Electric Vehicles // *IEEE Access*. – 2024. – Vol. 12. – P. 43984–43999. – DOI: 10.1109/ACCESS.2024.3380452.
Lv Z., Ou S., Jing H., Wu G., Shi D. Self-supervised learning for electric vehicle battery remaining useful life prediction using real-world unlabeled data // *Energy*. – 2026. – Vol. 356. – Art. 141302. – DOI: 10.1016/j.energy.2026.141302.
Hossain M. N., Rahman M. M., Ramasamy D. Advances in intelligent vehicular health monitoring and fault diagnosis: Techniques, technologies, and future directions // *Measurement*. – 2025. – Vol. 253, Part B. – Art. 117618. – P. 1–22. – DOI: 10.1016/j.measurement.2025.117618
Mahale Y., Kolhar S., More A. S. A comprehensive review on artificial intelligence driven predictive maintenance in vehicles: Technologies, challenges and future research directions // *Discover Applied Sciences*. – 2025. – Vol. 7. – Art. 243. – DOI: 10.1007/s42452-025-06681-3
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