A Non-Parametric Empirical Method for Nonlinear and Non-Stationary Signal Analysis

Abstract

A Non-parametric Ensemble Empirical Mode Decomposition (NCEEMD) method is a novel technique for nonlinear and non-stationary signal analysis to detect a gearbox fault. The NCEEMD method was based on the CEEMD, but the Gaussian white noise was replaced by the fractional Gaussian noise. The NCEEMD method does not need to choose the appropriate SNR and the number of ensemble trials before signal processing, which makes it a non-parametric method. This new approach was evaluated using a simulated malfunction signal representing two typical faults in gearbox systems: modulation and rub-impact. Its performance was evaluated in terms of MSE and computation time. A comparative study between the EMD, EEMD, CEEMD, and NCEEMD methods showed that the latter performed better by improving the computation time and accuracy of CEEMD. The proposed method is a non-parametric method that provides a powerful tool in extracting the modulation and the rub-impact features from a vibration signal. The NCEEMD method helps to track down the gearbox faults and resolve this crucial problem in mechanical machines.

Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects

“…But, in the NPCEEMD algorithm, the fractional gaussian noise (FGN) is replaced by the Gaussian white noise [31]. The fractional Gaussian Noise (FGN) is a stationary Gaussiancentred random process, generated periodically by sampling the fractional Brownian motion phenomenon (𝐵 𝐻 (𝑡)).…”

“…The non-parametric ensemble empirical mode decomposition (NPCEEMD) [31] is proposed as a solution to two problems, mode mixing and parametric dependent. The NPCEEMD methodology works on the principles, similar to CEEMD.…”