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The vibration is an important feature of the motor to diagnose the different faults. The existing invasive and non-invasive methods to capture and analysis vibration signals have many limitations. Thus, this work proposes a technique to capture and process the motor vibrations non-invasively to diagnose the multiple bearing faults using a microwave signal and software low pass filter respectively. The proposed method uses a high-frequency signal from microwave sensor (handheld Ultra-Wide Band (UWB) radar) projected on the Squirrel Cage Induction Motor (SCIM) and the reflected signal captured. The signal obtained is filtered with Software Phase Locked Loop (Low pass filter (SPLL)) and analyzed with a signal processing algorithm like Wavelet Transform to identify the faults in the motor. In this paper multiple bearing faults under no-load and full-load and a combination of bearing and rotor bar faults are diagnosed with the proposed method using Rational Dilation Wavelet Transforms (RDWT). The various bearing fault signal's energy at the sub-band-7 compared under normal and fault conditions. The signal energy at the fault frequency sub-band under no-load increases by 2.11%, 23.5% and 42.5% compared with the no-fault condition with the increase in the number of bearing faults from 1 to 3. The signal energy variation indicates the severity of the defects and the accuracy of the proposed method is verified with the contact method using a vibration sensor (accelerometer). The other faults analyzed are the combination of the bearing and rotor bar faults with the variation of the signal energy at sub-band 7 & 6. The variation of the signal energy for bearing and rotor bar faults are verified with the theoretical calculation and the proposed method detects the faults with the accuracy of approximately 93%. On the other hand, the proposed method is simple and cost-effective compared with the existing methods.
Madhusudhana Reddy Barusu (Tue,) studied this question.