A Non-Parametric Empirical Method for Nonlinear and Non-Stationary Signal Analysis
Received: 24 November 2021 | Revised: 4 December 2021 and 8 December 2021 | Accepted: 11 December 2021 | Online: 12 February 2022
Corresponding author: Y. Berrouche
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.
Keywords:
complementary ensemble empirical mode decomposition, fractional Gaussian noise, gearbox fault detection, nonlinear signal analysisDownloads
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