A self-adaptive time-frequency analysis method based on local mean decomposition and its application in defect diagnosis

Abstract

Local mean decomposition is a self-adaptive analysis method, which is suitable for the processing of multi-component amplitude-modulated and frequency-modulated signals. In this paper, the evaluating indicator of the end effect is put forward to characterize the advantages of local mean decomposition in non-stationary signal analysis. The comparisons between local mean decomposition and empirical mode decomposition show that the capabilities of the local mean decomposition on restraining the end effect and resisting the mode mixing are superior to those of empirical mode decomposition. Wigner–Ville distribution has had an important influence on non-stationary signal analysis, which keeps a higher time-frequency resolution and obtains superior distribution, but the cross-terms are its fatal disadvantages. Therefore, a new time-frequency analysis method, called self-adaptive Wigner-Ville distribution based on local mean decomposition, is proposed to effectively analyze non-stationary amplitude-modulated and frequency-modulated signals. Its validation is performed for the applications in a numerical signal, a practical gearbox vibration signal and a rotor oil whirl signal. The proposed method can analyze the multi-component signal with multiple frequency components, and evidently remove the cross-terms of Wigner-Ville distribution, and keep all its advantages. The study provides new means for state detection and fault diagnosis.