Enhancement of adaptive mode decomposition via angular resampling for nonstationary signal analysis of rotating machinery: Principle and applications

摘要

Vibration signal analysis provides an effective approach for condition monitoring and fault diagnosis of rotating machines. Under time-varying conditions, vibration signals feature nonstationarity, consist of multiple frequency components, and usually have spectral overlaps. Adaptive mode decomposition methods can extract mono-components from a given multi-component signal to meet the requirement for estimating instantaneous frequency. Among them, the recently proposed methods, including empirical wavelet transform, vari-ational mode decomposition and Fourier decomposition method, outperform the classic empirical mode decomposition in terms of rigorous mathematical formulation. Nevertheless, for multi-component signals with spectral overlaps, these three methods are subject to mode mixing and/or integrity issues, and fail to extract true mono-components, because they essentially separate mono-components based on spectral segmentation. In this paper, we propose a framework by exploiting the capability of angular resampling to address the mono-component overlapping issue. This methodology can separate true mono-components, thus facilitating accurate estimation of instantaneous frequency through Hilbert transform and generating perfect time-frequency representations (TFRs). Firstly, angular resampling is employed to make the constituent mono-components well separable in the frequency domain. Then, true mono-components are separated through adaptive mode decomposition. Next, informative mono-components are selected for further processing based on the prominent order information, and the selected mono-components are mapped into the time domain according to the relationship between the equal time and equal angle sampling. Finally, the instantaneous frequency and amplitude envelope of the recovered mono-components are calculated via Hilbert transform, and the TFR of raw signal is obtained by superposing the TFRs of all recovered mono-components. By doing so, the TFR achieves a fine time-frequency resolution and is free of both outer and inner interferences. The proposed methodology is demonstrated by simulated signal analysis, and further validated using the vibration data sets of three typical rotating machines (including a planetary gearbox in a wind turbine drivetrain, a civil aircraft engine and a hydraulic turbine rotor). The analysis results show its excellent capability to reveal the time-varying features of rotating machinery nonstationary signals.