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Bearing fault diagnosis using wavelet domain operator-based signal separation

This paper presents a new bearing fault diagnosis approach using wavelet domain operator-based signal separation. The measured vibration signal is first preprocessed using the continuous wavelet transform (CWT) to filter unwanted noise. Then an operator-based signal separation approach, called null space pursuit (NSP), is applied to decomposing the signal into a series of subcomponents and residues in accordance with their characteristics. Subsequently, the selected subcomponent with the maximum Kurtosis value is analyzed by the envelop spectrum to identify potential fault-related characteristic frequency components. Experimental studies from a real wind turbine gearbox have verified the effectiveness of the presented approach for bearing fault diagnosis.