Multifractal characterization of plunger pump vibration signal through improved empirical mode decomposition based detrended fluctuation analysis

Abstract

Promptly and accurately detecting the plunger pump fault in the hydraulic system is a serious issue in terms of improving reliability and decreasing accidents. A main work is analyzing the character of the collected samples. We used an improved empirical mode decomposition (EMD) based multifractal detrended fluctuation analysis (MFDFA) to extract the multifractal characters. The current method utilizes intrinsic mode functions (IMFs) selection and Kolmogorov — Smirnov test (K-S test) in the detrending procedure. The IMFs selection is used to deal with the undesired IMFs, and the two-sample K-S test works on each IMF and Gaussian noise to detect the noise-like IMFs. The proposed method adaptive to the nature of data and weakening the effect of noise make this approach work well for the non-stationary signal from the real system. We used the proposed method on the plunger pump vibration signal in the hydraulic system to verify the present of multifractal.