Inner Ring Fault Bearing Diagnosis in Induction Motor by Stator Current Envelope Analysis

Abstract

Bearing faults are most common in induction machines (IM), which are indispensable driving motors in modern industry due to their performance in terms of efficiency, simplicity, control, and maintenance. Faults in the inner and outer rings constitute the majority of the most common faults examined, and therefore their diagnosis requires particular attention. The classification and intelligent distinction between a healthy state and a faulty state is the trend in recent literature, but with the emergence of advanced induction machine controls, it is necessary to be vigilant about the effect of the control on the processed signals, as it can hide the fault effect in the different signals. Therefore, the use of alternative methods is a necessity. In this paper, We demonstrated the challenge of intelligent classification in distinguishing between healthy and defective states, particularly in the case of inner ring bearing fault (IRBF) of the IM bearing, using two classifiers that gave the best results. We have also illustrated the benefit of stator current envelope analysis (SCEA), which has proven its effectiveness by showing the spectrum indicating the IRBF, particularly when using an enhanced fuzzy logic-based direct torque control (FLDTC), compared to standard direct torque control (DTC), this spectrum is hidden in the spectral noise. The simulations are done in the MATLAB/Simulink environment.