Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis

Abstract Due to the complexity of mechanical system, multiple faults may co-exist in a rotating machinery, where vibration is commonly used for diagnosis. The measured vibration signal could be considered as a result of convolution process of malfunction induced periodic impact signal and resonant response of the mechanical component, and deconvolution is an effective way to restore impulses. The minimum entropy deconvolution (MED) has been shown to be an effective deconvolution method and has been employed in rotating machinery fault diagnosis. Nevertheless, the simulation in this paper shows that the MED is unable to identify multi-faults of rotating machinery fully when different faults excite different resonance frequencies. To overcome this shortcoming, a new multi-faults detection method based on Spectral kurtosis (SK) and MED is proposed. The effectiveness of the proposed method is validated by simulation data and field signals from a vacuum pump.

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