Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

Abstract

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

“…The presented method can be applied to dynamic signals in non-stationary conditions due to the use of the EMD and wavelet analysis methods. Previous works [32] used the EMD analysis and the MF-DFA algorithm to diagnose the damage. However, the WLMF algorithm shows lower computational costs, numerical stability, and high versatility in terms of real signals compared to MF-DFA.…”

“…Detrended fluctuation analysis MF-DFA is a commonly used algorithm for multifractal analysis. The method is often used to diagnose damage to rolling bearings [31][32][33] and gears [34][35][36]. In [37], the MF-DFA algorithm was used for the analysis of frictional vibrations, where the ensemble empirical mode decomposition EEMD was used to denoise the signal.…”

Application of Improved Bp Neural Network Using Genetic Algorithm in Fault Assessment of Bearing in Electromechanical System

“…However, most of the current studies focus on denoising and feature extraction of bearing fault signals, and then a variety of methods are used to classify and identify them [8]. Although the research obtains much useful information, the process is too complex and time-consuming to achieve the expected results [9]. Nowadays, the continuous development of deep learning theory based on machine learning replaces machine learning and is widely used in various fields [10].…”

Analysis of hydro-turbine non-stationary vibration signals based on CEEMDAN and Wigner-Ville Distribution

“…Empirical mode decomposition (EMD), which is based on the local characteristic time-scale of the data, can be used in nonlinear and non-stationary processes and has been widely applied in many fields successfully [6]. However, there are some inherent limitations in EMD, including the mode mixing, end effects and intrinsic mode function (IMF) criteria.…”