Separation of Impulse from Oscillation for Detection of Bearing Defect in the Vibration Signal
Toward ever improvement technology, a diagnostic procedure making use of Dual Q-Factor wavelet decomposition (DQWD) and adaptive wavelet transform (AWT) is proposed for the extraction of weak bearing defect feature. The vibration signal of bearing consists of mix of transient impulse (low-Q factor) and oscillatory signal (high-Q factor signal). Therefore, to separate the two different behavioral signals, Dual Q-factor wavelet decomposition is carried out. The DQWD decompose any signal into low-Q factor and high-Q factor signal. Further, extraction of feature is carried out by adaptive wavelet transform. For this adaptive wavelet is extracted from the low-Q factor signal using least square fitting method. The generated wavelet is applied to low-Q factor signal to produce AWT scalogram. Then, coefficients of resulting scalogram are integrated with respect to scale for each time segment. Then, envelope demodulation is applied to the resulting waveform to spot the defect frequency. An experimental study is presented to show the effectiveness of the proposed method. The proposed method is also effective over EMD and EEMD technique in isolating the transient impulse of defect from the oscillatory part of the signal.
KeywordsDual Q-factor wavelet decomposition (DQWD) Bearing Tunable Q-factor wavelet transform (TQWT) Oscillation Impulse
The authors would like to thanks, reviewers, for their suggestions which have enhanced the presentation.
- Kumar A, Kumar R (2017a) Least square fitting for adaptive wavelet generation and automatic prediction of defect size in the bearing using Levenberg–Marquardt Backpropagation. J Nondestruct Eval 36(1). https://doi.org/10.1007/s10921-016-0385-1