Open AccessBroken rotor bar fault detection using Hilbert transform and neural networks applied to direct torque control of induction motor drive
Author(s) -
Ramu Senthil Kumar,
Irudayaraj Gerald Christopher Raj,
Subramani Saravanan,
Subramaniam Umashankar
Publication year - 2020
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2019.1543
Subject(s) - control theory (sociology) , induction motor , sideband , stator , rotor (electric) , hilbert transform , fault (geology) , artificial neural network , direct torque control , torque , bar (unit) , computer science , matlab , engineering , artificial intelligence , physics , control (management) , spectral density , electrical engineering , voltage , radio frequency , seismology , geology , meteorology , thermodynamics , operating system , telecommunications
This study proposes a new approach for the detection of broken rotor bar (BRB) fault in three phase induction motor drive using Hilbert transform (HT) and artificial neural networks (ANNs), where the machine is controlled by direct torque control (DTC). HT is preferred to develop the stator current envelope. The sideband frequency and its amplitude of the samples are the input for the ANN. By using fast Fourier transform, the amplitude and frequency components are extracted and the severity of fault is determined by comparing the magnitude of an average of sideband frequency with the fundamental frequency. High accuracy identification of fault is found by ANN, where the results are trained and tested to a minimum mean square error that will detect the number of BRB in the induction motor. DTC is adopted for a suitable control technique in the industrial drives system to maintain good performance in torque control. The performance of the proposed method is verified by using MATLAB/SIMULINK and experimental tests.