An Optimal Ensemble Empirical Mode Decomposition Method for Vibration Signal Decomposition

Abstract

The vibration signal decomposition is a critical step in the assessment of machine health condition. Though ensemble empirical mode decomposition (EEMD) method outperforms fast Fourier transform (FFT), wavelet transform, and empirical mode decomposition (EMD) on nonstationary signal decomposition, there exists a mode mixing problem if the two critical parameters (i.e., the amplitude of added white noise and the number of ensemble trials) are not selected appropriately. A novel EEMD method with optimized two parameters is proposed to solve the mode mixing problem in vibration signal decomposition in this paper. In the proposed optimal EEMD, the initial values of the two critical parameters are selected based on an adaptive algorithm. Then, a multimode search algorithm is explored to optimize the critical two parameters by its good performance in global and local search. The performances of the proposed method are demonstrated by means of a simulated signal, two bearing vibration signals, and a vibration signal in a milling process. The results show that compared with the traditional EEMD method and other improved EEMD method, the proposed optimal EEMD method automatically obtains the appropriate parameters of EEMD and achieves higher decomposition accuracy and faster computational efficiency.

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“…It can be seen here that the performance of EEMD depends to a large extent on the selection of two important parameters: the amplitude of white noise and the number of overall tests [25].Therefore, EEMD can decompose the original data signals into several IMF, and eliminate pattern aliasing. But, it over-dependence on the selection and optimization of parameters which affects the effective decomposition of signals and cause lower accurate prediction.…”

“…The waveform frequencies of IMF2 and IMF3 are similar, but they are located in different IMF components. Thus, it can be seen that intermittency not only causes severe aliasing in time-frequency distribution, but also makes the physical meaning of a single IMF vague [25].…”

“…In discussing the optimal EEMD, Du et al pointed out that the traditional EEMD was mainly due to the decomposition process [25]. If the amplitude of the additional noise is too small relative to the original signal, modal mixing cannot be effectively eliminated.…”

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“…But, this transform suffers from mode mixing and end effect. An optimal ensemble empirical mode decomposition and improved Hilbert Huang transform with waveform matching technique are proposed in Li et al to diminish these effects. In Patnaik and Dash, authors have proposed a technique based on modified ADALINE and adaptive neuro‐fuzzy information system for detection and classification of islanding as well as non‐islanding PQ disturbances in DG system comprising a wind turbine and PV array.…”

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“…That is to say, a feature set is ideal if the system response can be reconstructed from it. Various time [1][2][3][4][5], frequency [6][7][8][9][10][11], and time-frequency [6,[12][13][14][15] features have been developed and used in the literature, each of which has its own strengths and weaknesses. Conventional time-domain statistical features are simple to calculate and are not problem exclusive; i.e., they can be applied to any dynamical system regardless of its nature.…”