Application of EMD-WVD and particle filter for gearbox fault feature extraction and remaining useful life prediction

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Abstract

Fault feature extraction and remaining useful life (RUL) prediction are important to condition based maintenance (CBM). In order to realize the fault feature extraction of gearbox vibration signal presenting nonlinear and non-Gaussian, the integration of empirical mode decomposition (EMD) and Wigner-Ville distribution (WVD) are proposed in this paper. Taking the kurtosis as standard, the WVD is applied to some IMFs with larger kurtosis to calculate the time-frequency distribution, with an effective suppress on mode mixing and the cross-term interference. Afterwards, particle filter (PF) with the state space model based on Wiener process is proposed to predict the RUL of gearbox considering degradation feature, gearbox teeth wear and nonlinear and non-Gaussian system. The gearbox life cycle test shows that the EMD-WVD method can extract the valued characteristics of vibration signal accurately, and the particle filter can provide an effective way to predict the RUL of gearbox.

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Liu, X., Jia, Y., He, Z., & Zhou, J. (2017). Application of EMD-WVD and particle filter for gearbox fault feature extraction and remaining useful life prediction. Journal of Vibroengineering, 19(3), 1793–1808. https://doi.org/10.21595/jve.2017.17680

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