Abstract
Motor bearings are one of the most critical components in rotating machinery. Envelope demodulation analysis has been widely used to demodulate bearing vibration signals to extract bearing defect frequency components but one of the main challenges is to accurately locate the major fault-induced frequency band with a high signal-to-noise ratio (SNR) for demodulation. Hence, an enhanced fault detection method combining the maximal overlap discrete wavelet packet transform (MODWPT) and the Teager energy adaptive spectral kurtosis (TEASK) denoising algorithms is proposed for identifying the weak periodic impulses. The Teager energy power spectrum (TEPS) defines the sparse representation of the filtered signals of the MODWPT in the frequency domain via the Teager energy operator (TEO); the TEASK helps determine the most informative frequency band for demodulation. The methodology is compared in terms of performance with the fast Kurtogram and the Autogram methods. The simulation and practical application examples have shown that the proposed MODWPT-TEASK method outperforms the above two methods in diagnosing defects of motor bearings.
Highlights
Rolling element bearings are one of the most critical supporting components in rotating electrical machinery due to the fact that approximately 41–42% of induction machine failures arise from faulty bearings [1,2]
When a defect appears on the bearing surface, the rolling elements pass over the defects and generate a series of periodic impulses to excite higher frequency bearing resonances during this process; the diagnostic information related to bearing faults is submerged in the heavy background noise and other vibration sources, increasing the difficulty of bearing fault detection
This paper proposes a new bearing diagnostic method to determine the fault sensitive frequency bands, where the weak periodic impulses can be accurately revealed and identified in the presence of strong noise
Summary
Rolling element bearings are one of the most critical supporting components in rotating electrical machinery due to the fact that approximately 41–42% of induction machine failures arise from faulty bearings [1,2]. Vibration signals measured from bearings usually contain rich information on the machine condition and have been widely used for bearing fault diagnosis [2]. When a defect appears on the bearing surface, the rolling elements pass over the defects and generate a series of periodic impulses to excite higher frequency bearing resonances during this process; the diagnostic information related to bearing faults is submerged in the heavy background noise and other vibration sources, increasing the difficulty of bearing fault detection. When impacts induced by rolling bearings, they will excite the resonance of bearing elements and housing structure, which causes the modulation of vibration signals in the high frequency bands. Envelope demodulation has been widely used to demodulate vibration signals to extract bearing characteristic defect frequencies, which strongly require vibration data with high signal-to-noise ratio (SNR) [3,4]. To enhance the SNR and accentuate fault features, a band-pass filter is usually set manually around the desired resonant frequency band before demodulation is performed; the main challenge in envelope demodulation is to find a suitable frequency band for amplitude demodulation [5]
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