PHYSICS-INFORMED HYBRID MODELING FOR PREDICTIVE CONDITION MONITORING OF A GEAR-DRIVEN COTTON GIN MACHINE

Authors

  • Yunusov Odiljon Makhmudjon ogli Namangan State University Author
  • Saydaliyev Abdulbori Abdulvohid ogli Namangan State University Author
  • Khoshimov Adxam Akhmadjon ogli Author
  • Sharifbayev Rakhinjon Nasir ogli Namangan State Technical University Author
  • Khurshud Madaliyev Bahrom ogli Namangan State Technical University Author
  • Knyazev Mikhail Aleksandrovich Belarusian National Technical University Author

Keywords:

Predictive maintenance, hybrid modeling, vibration diagnostics, gearbox dynamics, Kalman filter, condition monitoring, cotton gin machine, industrial analytics.

Abstract

Rotating machinery reliability remains a cornerstone of industrial productivity, particularly in cotton processing plants where gin machines operate under highly variable mechanical loads. Unexpected drivetrain failures may cause substantial production losses and energy inefficiencies. This study proposes a physics-informed hybrid modeling framework for predictive condition monitoring of a gearbox-driven cotton gin machine powered by a 75 kW induction motor.

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Published

2026-02-26

Issue

Vol. 2 No. 2 (2026)

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

PHYSICS-INFORMED HYBRID MODELING FOR PREDICTIVE CONDITION MONITORING OF A GEAR-DRIVEN COTTON GIN MACHINE. (2026). Modern American Journal of Engineering, Technology, and Innovation, 2(2), 8-19. https://usajournals.org/index.php/2/article/view/2012