No abstract

No abstract

No abstract

This study analyses the vibration of Squirrel Cage Induction Motor (SCIM) with bearing faults using highfrequency signal (Handheld UWB radar) and Software Phase Locked Loop algorithm (SPLL). The contact or non-contact methods perform condition monitoring of the SCIM. The proposed method is a non-contact technique to perform condition monitoring of the SCIM. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signals analysis; whereas non-contact methods accomplish via acoustic, current and stray flux measurement. The existing techniques suffer from the influence of adjoining electrical machines; require human expertise to mount sensors and analysing the signals. In this paper, a new, non-contact method proposed for bearing fault identification in the SCIM. The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The signal obtained is analysed with an advanced signal processing algorithm like Rational Dilation Wavelet Transform (RDWT) to identify the faults in the SCIM. The signal energy at the fault frequency level increases from 4.72 % to 5.82 % with the increase in the number of the faults. The signal energy variation indicates the severity of the faults. From the experimental results, the bearing fault of the motor identified in the beginning stage of the fault.

The condition monitoring of an induction motor performs by contact or non-contact methods. The contact methods execute via vibration signal, temperature measurement, current signature analysis and so on, and the non-contact methods accomplish via thermal imaging, temperature measurement and acoustic emission measurement. The contact method requires human expertise to evaluate failures and it is laborious. The authors propose a novel, low-cost and non-contact method using software phase locked loop (SPLL) for electrical fault identification in the three-phase squirrel cage induction motor. The handheld Doppler ultra wideband radar transmitted signal is focused on the induction motor and the reflected signal is analysed with SPLL. The SPLL error signal correlates the faults. From the experimental results, the fault identification of motor in the earlier stage achieves better accuracy of the different motor fault condition. The pattern of error signal provides the information of various faults in squirrel cage induction motor.