Application of fast singular spectrum decomposition method based on order statistic filter in rolling bearing fault diagnosis

Abstract

Singular spectrum decomposition (SSD) is a new adaptive signal processing method for nonlinear and non-stationary signals. By constructing a trajectory matrix and adaptively selecting the embedding dimensions, the method divides non-stationary signals into several single-component signals successively from high frequency to low frequency. However, in the process of component reconstruction, bandwidth estimation and determining sizable trends by building a Gaussian function superposition spectral model are extremely complicated. Moreover, the parameter setting requires too much manual intervention and lacks theoretical support. Hence, aimed at nonlinear and non-stationary vibration signals of rolling bearings, a novel method of fault feature extraction based on the order statistic filter (OSF) for fast SSD (FSSD) is proposed. The FSSD method adopts the envelope method of OSFs to divide the spectrum and determine the sizable trend to improve the process. The proposed method is applied to bearing fault diagnosis. The analysis results of simulation signals and bearing experimental signals show that the new method can decompose signals quickly, effectively and accurately, and the mode mixing and time-consuming problems are refined.