Period Enhanced Feature Mode Decomposition and Its Application for Bearing Weak Fault Feature Extraction

Abstract Decomposition methods which can separate the fault components into different modes have been widely applied in bearing fault diagnosis. However, early fault diagnosis is always a challenge for the signal processing methods as well as the traditional decomposition methods due to the heavy noise. Therefore, how to extract the weak fault information from the complicated signal with low SNR is of significance. To overcome this issue, a period-enhanced feature mode decomposition (PEFMD) method is proposed in this paper. Firstly, the initialized filters used for the mode decomposition are adaptively designed according to the spectrum of the original vibration signal. Secondly, time synchronized averaging is used in the iterative process to excavate and identify accurately the weak period components and determine the period of the iterative signal. Finally, the period information can promote the proposed method to decompose the fault component into the hopeful modes by setting correlation kurtosis as the optimation objective and the mode selection. The practicability and superiority of the proposed PEFMD are verified by the simulated and experimented data. Compared with the feature mode decomposition method and variational mode decomposition, the proposed decomposition method shows an obvious performance advantage under low SNR situations.