Abstract
When a localized defect is induced, the vibration signal of rolling bearing always consists periodic impulse component accompanying with other components such as harmonic interference and noise. However, the incipient impulse component is often submerged under harmonic interference and background noise. To address the aforementioned issue, an improved method based on resonance-based sparse signal decomposition with optimal quality factor ( Q-factor) is proposed in this paper. In this method, the optimal Q-factor is obtained first by genetic algorithm aiming at maximizing kurtosis value of low-resonance component of vibration signal. Then, the vibration signal is decomposed based on resonance-based sparse signal decomposition with optimal Q-factor. Finally, the low-resonance component is analyzed by empirical model decomposition combination with energy operator demodulating; the fault frequency can be achieved evidently. Simulation and application examples show that the proposed method is effective on extracting periodic impulse component from multi-component mixture vibration signal.
Highlights
If rolling bearing occurs local defects, the corresponding vibration signal will present stochastic feature that contains multi-frequency components
Considering the rolling bearing is often under complex working condition, the corresponding vibration signal comprises periodic impulse component which presents fault information and rotating harmonic component and noise, and the impulse component is generally weaker than interfering component
empirical model decomposition (EMD) is applied for low-resonance component to obtain the optimal IMF which utilizes Teager energy operator, and envelope demodulation spectrum is presented in Figure 16, it appears fault characteristic frequency fo and the fault characteristic frequency is significant compared with other components
Summary
If rolling bearing occurs local defects, the corresponding vibration signal will present stochastic feature that contains multi-frequency components. Keywords Fault diagnosis, resonance-based sparse signal decomposition, optimal Q-factor, genetic algorithm, energy operator demodulating, rolling bearing
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