Sun’iy intellekt va mashinali o‘qitish asosida transport vositalari nosozliklarini diagnostika qilish va prognozlash: zamonaviy yondashuvlar sharhi
##semicolon##
transport vositalari, sun’iy intellekt, mashinali o‘qitish, texnik diagnostika, nosozliklarni aniqlash, nosozliklarni prognozlash, prediktiv texnik xizmat, ekspluatatsion ishonchlilik, CAN-bus, OBD, neyron tarmoqlar, qoldiq resursAbstrak
Mazkur maqolada transport vositalarining texnik holatini nazorat qilish, nosozliklarni erta aniqlash va ularning yuzaga kelish ehtimolini prognozlashda sun’iy intellekt hamda mashinali o‘qitish texnologiyalaridan foydalanishning zamonaviy yondashuvlari tahlil qilinadi. Transport vositalarining ekspluatatsion ishonchliligini oshirish, texnik xizmat ko‘rsatish xarajatlarini kamaytirish va kutilmagan nosozliklarning oldini olishda ma’lumotlarga asoslangan diagnostika tizimlarining ahamiyati yoritiladi. Mashinali o‘qitishning nazorat qilinadigan va nazoratsiz algoritmlari, neyron tarmoqlar, chuqur o‘qitish, ansambl modellari hamda prediktiv texnik xizmat ko‘rsatish usullarining qo‘llanish xususiyatlari umumlashtiriladi. Shuningdek, CAN-bus, OBD, telematik tizimlar va turli sensorlardan olinadigan ekspluatatsion ma’lumotlarni qayta ishlash asosida nosozliklarni tasniflash va qoldiq resursni baholash imkoniyatlari ko‘rib chiqiladi. Tahlil natijasida intellektual diagnostika tizimlarining afzalliklari, mavjud cheklovlari va kelgusidagi rivojlanish yo‘nalishlari aniqlanadi.
##submission.citations##
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
