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- W2953669244 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." @default.
- W2953669244 created "2019-07-12" @default.
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- W2953669244 date "2019-06-28" @default.
- W2953669244 modified "2023-09-26" @default.
- W2953669244 title "Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal <i>Q</i>-factor" @default.
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- W2953669244 doi "https://doi.org/10.1177/0020294019858181" @default.
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