Abstract

Vibration signals of rolling element bearing’s early weak fault are often submerged by some interference components. To extract early weak fault features accurately, a weak fault feature enhancement method of rolling element bearing based on resonance sparse decompositionand multi-objective information frequency band selection is proposed. This method makes full use of resonance sparse decomposition in filtering the interferences and multi-objective information frequency band selection in enhancing impulsive and cyclostationary features of rolling element bearing’s early weak fault simultaneously. First, resonance sparse decomposition is used as the preprocessing program of multi-objective information frequency band selection to filter the interferences (such as rotating frequency with its harmonics) of rolling element bearing’s early weak fault characteristic components. Then, the filtered components containing main fault information are analyzed by MIFBS to establish the best band-pass filter. Finally, the envelope spectrum analysis is applied on the filtered vibration signal, and the fault characteristic frequency with its harmonics is extracted. To achieve the optimal output parameters of multi-objective information frequency band selection, fusion indexes based on time- and frequency-domain estimators are proposed and used to balance the enhancement of impulsive and cyclostationary characteristics of rolling element bearing’s early weak fault signals. Compared with most of the existing methods mainly based on single time-domain estimators or frequency-domain estimators to improve performance spectral kurtosis, the proposed fusion indexes overcome their defects and could enhance the impulsive and cyclostationary features of rolling element bearing’s early weak fault simultaneously. Effectiveness of the proposed method is verified through simulation and experiment. Besides, its advantage over the other related methods is also presented.

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