scholarly journals Multiple faults detection and identification of three phase induction motor using advanced signal processing techniques

Author(s):  
Majid Hussain ◽  
Rana Rizwan Ahmed ◽  
Imtiaz Hussain Kalwar ◽  
Tayab Din Memon
2021 ◽  
Vol 3 (1) ◽  
pp. 61-76
Author(s):  
Thomas Amanuel ◽  
Amanuel Ghirmay ◽  
Huruy Ghebremeskel ◽  
Robel Ghebrehiwet ◽  
Weldekidan Bahlibi

Signal processing is considered as an efficient technique to detect the faults in three-phase induction motors. Detection of different varieties of faults in the rotor of the motor are widely studied at the industrial level. To extend further, this research article presents the analysis on various signal processing techniques for fault detection in three-phase induction motor due to the damages in rotor bar. Usually, Fast Fourier Transform (FFT) and STFT are used to analyze the healthy and faulty motor conditions based on the signal characteristics. The proposed study covers the advantages and limitations of the proposed wavelet transform (WT) and each technique for detecting the broken bar of induction motors. The good frequency information can be collected from FFT techniques for handling multiple faults identification in three-phase induction motor. Despite the hype, the detection accuracy gets reduced during the dynamic condition of the machine because the frequency information on sudden time changes cannot be employed by FFT. The WT method signal analysis is compared with FFT to propose fault detection method for induction motor. The WT method is proving better accuracy when compared to all existing methods for signal information analysis. The proposed research work has simulated the proposed method with MATLAB / SIMULINK and it helps to effectively detect the healthy and faulty conditions of the motor.


1999 ◽  
Vol 14 (2) ◽  
pp. 147-152 ◽  
Author(s):  
M.E.H. Benbouzid ◽  
H. Nejjari ◽  
R. Beguenane ◽  
M. Vieira

2020 ◽  
Vol 10 (9) ◽  
pp. 3334 ◽  
Author(s):  
Sanaz Roshanmanesh ◽  
Farzad Hayati ◽  
Mayorkinos Papaelias

In this paper the application of cyclostationary signal processing in conjunction with Ensemble Empirical Mode Decomposition (EEMD) technique, on the fault diagnostics of wind turbine gearboxes is investigated and has been highlighted. It is shown that the EEMD technique together with cyclostationary analysis can be used to detect the damage in complex and non-linear systems such as wind turbine gearbox, where the vibration signals are modulated with carrier frequencies and are superimposed. In these situations when multiple faults alongside noisy environment are present together, the faults are not easily detectable by conventional signal processing techniques such as FFT and RMS.


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