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Moazzen, Mohammad Ebrahim, Abdoos, Ali-Akbar, Gholamian, Sayyed Asghar. (1405). A VMD–mRMR–Random Forest Framework for Intelligent Detection and Severity Assessment of Broken Rotor Bar Faults in Induction Motors. پژوهشنامه مدیریت اجرایی, 3(3), 1-10. doi: 10.22080/cste.2026.30790.1102
Mohammad Ebrahim Moazzen; Ali-Akbar Abdoos; Sayyed Asghar Gholamian. "A VMD–mRMR–Random Forest Framework for Intelligent Detection and Severity Assessment of Broken Rotor Bar Faults in Induction Motors". پژوهشنامه مدیریت اجرایی, 3, 3, 1405, 1-10. doi: 10.22080/cste.2026.30790.1102
Moazzen, Mohammad Ebrahim, Abdoos, Ali-Akbar, Gholamian, Sayyed Asghar. (1405). 'A VMD–mRMR–Random Forest Framework for Intelligent Detection and Severity Assessment of Broken Rotor Bar Faults in Induction Motors', پژوهشنامه مدیریت اجرایی, 3(3), pp. 1-10. doi: 10.22080/cste.2026.30790.1102
Moazzen, Mohammad Ebrahim, Abdoos, Ali-Akbar, Gholamian, Sayyed Asghar. A VMD–mRMR–Random Forest Framework for Intelligent Detection and Severity Assessment of Broken Rotor Bar Faults in Induction Motors. پژوهشنامه مدیریت اجرایی, 1405; 3(3): 1-10. doi: 10.22080/cste.2026.30790.1102

A VMD–mRMR–Random Forest Framework for Intelligent Detection and Severity Assessment of Broken Rotor Bar Faults in Induction Motors

Contributions of Science and Technology for Engineering
مقاله 7، دوره 3، شماره 3، آبان 2026، صفحه 1-10    اصل مقاله (906.53 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/cste.2026.30790.1102
نویسندگان
Mohammad Ebrahim Moazzen؛ Ali-Akbar Abdoos* ؛ Sayyed Asghar Gholamian
Department Of Electrical And Computer Engineering, Babol Noshirvani University Of Technology, Shariati Ave., Babol, Iran, Post Code:47148-71167
تاریخ دریافت: 30 آذر 1404،  تاریخ بازنگری: 28 دی 1404،  تاریخ پذیرش: 21 بهمن 1404 
چکیده
Induction motors are critical and expensive components of industrial sectors; hence, providing a reliable condition monitoring scheme is a serious issue. However, mechanical maloperation due to broken rotor bar (BRB) faults may degrade the motor performance significantly. If these severe faults are left undetected, secondary damages will be inevitable. This paper presents a new diagnosis method that uses time-frequency analysis to distinguish between healthy and BRB faults as well as fault severity. To achieve this, initially, vibration signals are decomposed into intrinsic mode functions using Variational Mode Decomposition (VMD) and some features are extracted from decomposed signals. Then, the minimum Redundancy Maximum Relevance (mRMR) method is employed to select the most effective features to enhance the accuracy and generalizability of detection scheme. Finally, these features are fed into a Random Forest (RF) classifier to detect the faulty condition and determine its severity. To implement and evaluate the proposed method, real-world vibration data collected from a 380 V, 4-pole induction motor is utilized. The obtained results indicate that the accuracy of the proposed method is quite superior to conventional methods in detecting the number of broken bars, despite its simplicity. Therefore, the proposed intelligent method can be effectively utilized in industrial applications.
کلیدواژه‌ها
Induction Motors؛ Broken Bar؛ Pattern Recognition؛ Signal Analysis؛ Feature Selection؛ Vibration Signals
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