Effectiveness of the frequency analysis of the stator current in the rotor fault detection of induction motors

2008 ◽  
Author(s):  
Czeslaw T. Kowalski ◽  
Waldemar Kanior
Keyword(s):  
Fault Detection ◽  
Frequency Analysis ◽  
Induction Motors ◽  
Stator Current

Combined Vibration and Stator Current Techniques for Induction Motors Fault Detection – A Review

2021 ◽  
Author(s):  
Julio Ayala ◽  
Matias Meira ◽  
Carlos Verucchi ◽  
Guillermo Bossio ◽  
Gerardo Acosta
Keyword(s):  
Fault Detection ◽  
Induction Motors ◽  
Stator Current

scholarly journals Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4102
Author(s):  
Tomas A. Garcia-Calva ◽  
Daniel Morinigo-Sotelo ◽  
Oscar Duque-Perez ◽  
Arturo Garcia-Perez ◽  
Rene de J. Romero-Troncoso
Keyword(s):  
Fault Detection ◽  
Frequency Analysis ◽  
Induction Motors ◽  
Spectral Estimation ◽  
Diagnostic Techniques ◽  
Frequency Resolution ◽  
Minimum Norm ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Stationary Conditions

In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.

A Kalman Filter Based Technique for Stator Turn-Fault Detection of the Induction Motors

2017 ◽  
Vol 18 (6) ◽  
Author(s):  
Teymoor Ghanbari ◽  
Haidar Samet
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
High Performance ◽  
Induction Motors ◽  
Simple Technique ◽  
Harmonic Distortion ◽  
Stator Current ◽  
Three Phase ◽  
Power Frequency ◽  
Faults Diagnosis

AbstractMonitoring of the Induction Motors (IMs) through stator current for different faults diagnosis has considerable economic and technical advantages in comparison with the other techniques in this content. Among different faults of an IM, stator and bearing faults are more probable types, which can be detected by analyzing signatures of the stator currents. One of the most reliable indicators for fault detection of IMs is lower sidebands of power frequency in the stator currents. This paper deals with a novel simple technique for detecting stator turn-fault of the IMs. Frequencies of the lower sidebands are determined using the motor specifications and their amplitudes are estimated by a Kalman Filter (KF). Instantaneous Total Harmonic Distortion (ITHD) of these harmonics is calculated. Since variation of the ITHD for the three-phase currents is considerable in case of stator turn-fault, the fault can be detected using this criterion, confidently. Different simulation results verify high performance of the proposed method. The performance of the method is also confirmed using some experiments.

Fault Detection of Induction Motors Using Just In Time Classifiers

2021 ◽  
Author(s):  
Saaeede Hazbavi ◽  
Roozbeh Razavi-Far ◽  
Mohammad Mehdi Arefi ◽  
Alireza Khayatian ◽  
Mehrdad Saif
Keyword(s):  
Fault Detection ◽  
Induction Motors ◽  
Just In Time
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