Outer race bearing fault identification of induction motor based on stator current signature by wavelet transform

Yeolekar, Saket; Mulay, G. N.; Helonde, J. B.

doi:10.1109/rteict.2017.8256951

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…In the proposed method vibration signal alone analysed, whereas, in other techniques, more than one signal (vibration, current, speed and acoustic signal) is required for fault identification. The signal analysis algorithms used in the other methods are FFT [9], [12], STFT [41], Hilbert-Huang Transform (HHT) [11], DWT [28], [40] and absolute value-based principal component analysis (AVB-PCA) [15], which suffer from unstable Q-factor, whereas, in the proposed method RDWT (constant Q-factor) is utilized for signal analysis.…”

Section: Comparison With Related Workmentioning

confidence: 99%

Diagnosis of Bearing Outer Race Faults Using a Low-Cost Non-Contact Method with Advanced Wavelet Transforms

Barusu¹,

Sethurajan²,

Meganathan³

2019

ElAEE

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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.

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