DIAGNOSTICS OF LOW-POWER ASYNCHRONOUS MOTORS USING ARTIFICIAL INTELLIGENCE

Authors

  • Adilov Nodir Botir ugli, Ziyamukhamedov Akil Tulkunovich, Toshtemirov Humoyun SHokir ugli, Goziev Kholiyorjon Olim ugli Tashkent State Transport University (Tashkent, Uzbekistan)

Keywords:

Low-power induction motor; Artificial intelligence; Condition monitoring; Magnetic flux density; Stator faults; Portable Tesla-meter; Predictive maintenance; Localized faults; Real-time monitoring.

Abstract

This study investigates the condition monitoring and fault diagnostics of low-power (0.1–10 kW) induction motors using artificial intelligence (AI) techniques. Low-power induction motors are widely employed in industrial, transport, agricultural, and automated technological systems, where their reliable operation is critical for maintaining continuous production processes. In this research, the magnetic flux (B) between the stator and rotor was measured using a Tesla meter to identify common fault zones. The results indicate that decreases in magnetic flux in certain stator segments reveal the presence of localized faults. Furthermore, AI algorithms enable automated analysis and classification of motor current, vibration, and magnetic flux signals, allowing early detection of faults without interrupting motor operation. These findings provide a scientific and practical foundation for optimizing maintenance strategies, reducing unplanned downtime, and improving the operational reliability of low-power induction motors.

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References

Kobayashi, A., Nakamura, K., & Ono, T. Measuring the Operating Condition of Induction Motor Using High-Sensitivity Magnetic Sensor. Sensors. 2025;25(14):4471. Bu maqolada induksion motorlarni yuqori sezgirlikdagi magnit sensor yordamida monitoring qilish va magnit signallar orqali ish holatini aniqlash usullari keltirilgan.

Vokhidov, M. The Magnetic Field in the Air Gap in the Presence of Rotor Eccentricity in Alternating Current Traction Motors. Acta of Turin Polytechnic University in Tashkent. 2025;15(1):51–55. Ishda asinxron motorlarda rotor eksentrisiteti sharoitida havo bo‘shlig‘i magnit maydoni modellari va hisobi tahlil qilingan.

Zamudio-Ramírez, I., Osornio-Rios, R.A., Antonino-Daviu, J., et al. Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review. IEEE Transactions on Industrial Informatics. 2021;18(5):2895–2908. Ushbu sharh maqolada elektromagnit flux (havo bo‘shlig‘i induksiyasi ham) asosida motor holatini monitoring qilish metodlari tahlil qilingan.

Wu, X., Song, J., Duan, Z., et al. Air gap flux density measurement of PMSLM based on TMR sensing external stray magnetic field and CNN-LSTM. Measurement. 2024; (Scopus-indexed maqola). Bu ishda mos ravishda havo bo‘shlig‘idagi magnit flux zichligini o‘lchash usullari va sensorlardan foydalanish ko‘rsatib o‘tilgan, metodologiya sizning tedqiqotingizga o‘xshashdir.

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Published

2026-01-24

How to Cite

Adilov Nodir Botir ugli, Ziyamukhamedov Akil Tulkunovich, Toshtemirov Humoyun SHokir ugli, Goziev Kholiyorjon Olim ugli. (2026). DIAGNOSTICS OF LOW-POWER ASYNCHRONOUS MOTORS USING ARTIFICIAL INTELLIGENCE. Journal of Applied Science and Social Science, 16(01), 624–627. Retrieved from https://www.internationaljournal.co.in/index.php/jasass/article/view/3068

Issue

Vol. 16 No. 01 (2026): Volume 16 Issue 01

Section

Articles

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