TEXNIK-AMALIY FANLARDA SUN’IY INTELLEKT VA RAQAMLI EGIZAKLAR INTEGRATSIYASI VA ENERGIYA SAMARADORLIGINI OSHIRISHNING INTELLЕKTUAL MODELLARI.

Mualliflar

  • Orifjonova Mohidil Farg`ona davlat texnika universiteti “Telekommunikatsiya muhandisligi” kafedrasi assistenti Muallif

DOI:

https://doi.org/10.65164/yy9nfe93

Kalit so‘zlar:

Sun’iy intellekt, Sanoat 4.0, LSTM algoritmi, Raqamli egizaklar, IoT, Bashoratli tahlil, Energiya samaradorligi.

Abstrak

Ushbu maqolada sun’iy intellekt (SI) va raqamli texnologiyalarning texnikamaliy
fanlar rivojidagi o‘rni tadqiq etiladi. Asosiy e’tibor sanoat ob’ektlarida energiya sarfini
optimallashtirish uchun LSTM (Long Short-Term Memory) neyron tarmoqlari va Raqamli egizaklar
(Digital Twins) texnologiyasini qo‘llashga qaratilgan. Tadqiqot davomida real vaqtdagi datchiklar
ma’lumotlari asosida energiyani bashorat qilish modeli ishlab chiqish natijada esa SI algoritmlarini
joriy etish orqali texnik tizimlarning samaradorligi 18% ga oshib, kutilmagan nosozliklar ehtimoli
32% ga kamayishi ko‘zda tutilgan.

Havolalar

[1]. Tao, F., & Liu, H. Digital twin-driven smart manufacturing: Connotation, reference model, and

applications. Robotics and Computer-Integrated Manufacturing, 61, 101837, 2020.

[2]. Zhang, M., Zuo, Y., & Tao, F. Equipment structural health monitoring based on digital

twin.

[3]. International Journal of Advanced Manufacturing Technology, 115(11), 3703-3715, 2021.

[4]. Wang, P., & Fan, Y. Research on high-precision belt scale weighing compensation algorithm

based on adaptive filtering. Measurement and Control, 54(7-8), 1023-1035, 2021.

[5]. Li, X., & He, D. Digital twin for industrial products: A multi-dimensional framework and key

technologies. Journal of Manufacturing Systems, 57, 12-25, 2022.

[6]. Chen, Z., & Huang, B. Multi-criteria decision making for sensor selection in aggressive

industrial environments using AHP-MCDM. Journal of Process Control, 112, 45-58, 2023.

Yuklab olishlar

Nashr qilingan

2026-04-14