An improved Hilbert vibration decomposition method for analysis of rotor fault signals

Abstract

The existence of false components with the Hilbert vibration decomposition (HVD) method has seriously restricted its application in practical rotor fault diagnosis. To solve this problem, an improved HVD method was proposed by adopting Kullback–Leibler (K–L) divergence values as a distinguishing index of true and false components, which is named the KL-HVD method. First, it calculated the K–L divergence values between the HVD components and the original signal, and then, these values are compared with the set threshold. Finally, it eliminated the false components whose K–L divergence values were larger than the threshold. The experimental results of rotor fault signal analysis demonstrated that the KL-HVD method could more accurately extract the time–frequency characteristics of the faults and the K–L divergence value was more suitable as the distinguishing index of true and false components than the mutual information and correlation coefficient values.