Singular component decomposition and its application in rolling bearing fault diagnosis

Abstract In the rolling bearing fault diagnosis based on vibration signal, the feature information always exists in the specific sideband of the signal. Singular value, as a mathematical quantity can represent the characteristic information of data, provides the theoretical support for extracting fault information from signals. Based on this, this paper proposes a new method called singular component decomposition. The signal is divided into components by the signal spectrum trend. Through linear transformation of Hankel matrix constructed by each component, the corresponding singular values are calculated. Combined with the amplitude filtering characteristic and the time domain negative entropy index, the effective singular values are screened out. The reconstructed components retain complete fault information for fault diagnosis of rolling bearings. The simulation and experimental results show that the
SCD can extract the effective information to the maximum extent to realize the fault diagnosis of the signal.