Extraction of Frictional Vibration Features with Multifractal Detrended Fluctuation Analysis and Friction State Recognition

For the purpose of extracting the frictional vibration characteristics of the friction pair during friction and wear in different friction states, the friction and wear tests of friction pair in different friction states were conducted on a testing machine. Higher-dimensional fractal and multifractal characteristics hidden in time series can be examined by multifractal detrended fluctuation analysis (MFDFA) method. The frictional vibration time-domain signals, the friction coefficient signals and the frictional vibration frequency-domain signals were analyzed and multifractal spectra were acquired by using the MFDFA algorithm. According to the spectra, the multifractal spectrum parameters of these signals were calculated to realize the quantitative characterization of frictional vibration characteristics in different friction states. The analysis shows that it is symmetric in the variation trends of the multifractal spectrum parameters of the frictional vibration signals and the friction coefficient data. Based on the multifractal spectrum parameters of frictional vibration, the principal component analysis (PCA) algorithm was applied to establish the friction state recognition method. The results show that the multifractal spectra and their parameters can characterize the frictional vibrations, and the friction state recognition can be realized based on the multifractal spectrum parameters of frictional vibrations.

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Multifractal Detrended Fluctuation Analysis of Frictional Vibration Signals in the Running-in Wear Process

Tribology Letters ◽

10.1007/s11249-017-0829-5 ◽

2017 ◽

Vol 65 (2) ◽

Cited By ~ 4

Author(s):

Jingming Li ◽

Haijun Wei ◽

Li Fan ◽

Lidui Wei

Keyword(s):

Detrended Fluctuation Analysis ◽

Fluctuation Analysis ◽

Vibration Signals ◽

Multifractal Detrended Fluctuation Analysis ◽

Wear Process ◽

Detrended Fluctuation ◽

Frictional Vibration

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Fish Sound Characterization Using Multifractal Detrended Fluctuation Analysis

Fluctuation and Noise Letters ◽

10.1142/s0219477520500091 ◽

2020 ◽

Vol 19 (01) ◽

pp. 2050009 ◽

Cited By ~ 1

Author(s):

Kranthikumar Chanda ◽

Shubham Shet ◽

Bishwajit Chakraborty ◽

Arvind K. Saran ◽

William Fernandes ◽

...

Keyword(s):

Hurst Exponent ◽

Detrended Fluctuation Analysis ◽

Linear Method ◽

Multifractal Spectrum ◽

Fluctuation Analysis ◽

Multifractal Detrended Fluctuation Analysis ◽

Generalized Hurst Exponent ◽

Estuarine Systems ◽

Detrended Fluctuation ◽

Sound Data

This work involves the application of a non-linear method, multifractal detrended fluctuation analysis (MFDFA), to describe fish sound data recorded from the open waters of two major estuarine systems. Applying MFDFA, the second-order Hurst exponent [Formula: see text] values are found to be [Formula: see text] and [Formula: see text] for the fish families Batrachoididae (common name: Toadfish) and Sciaenidae (common name: Croakers, drums), respectively. The generalized Hurst exponent [Formula: see text]-related width parameters [Formula: see text] are found to be [Formula: see text] and [Formula: see text], respectively, for toadfish and Sciaenidae vocalizations, implying greater heterogeneity and multifractal characteristics. The results suggest that the Sciaenidae fish calls are smoother in comparison with Batrachoididae. Clustering of multifractal spectrum-related parameters with respect to toadfish and Sciaenidae vocalization characteristics is observed in this analyses.

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Multifractal Detrended Fluctuation Analysis on Friction Coefficient During the Friction Process

Journal of Tribology ◽

10.1115/1.4036912 ◽

2017 ◽

Vol 140 (1) ◽

Cited By ~ 1

Author(s):

Guodong Sun ◽

Hua Zhu ◽

Cong Ding ◽

Yuankai Zhou

Keyword(s):

Friction Coefficient ◽

Detrended Fluctuation Analysis ◽

Tribological Behavior ◽

Multifractal Spectrum ◽

Fluctuation Analysis ◽

Friction Process ◽

Multifractal Detrended Fluctuation Analysis ◽

Aisi 52100 ◽

Aisi 1045 ◽

Detrended Fluctuation

The dynamic evolutionary law and tribological behavior of tribopair AISI 52100-AISI 1045 were studied via the multifractal method. Friction experiment was performed on a ring-on-disk tribometer under lubrication, and the multifractal detrended fluctuation analysis (MF-DFA) method was adapted to characterize the multifractality of the friction coefficient. The multifractal spectra first exhibited a left-hook, then right-hook, and left-hook, respectively, during the friction stages. The multifractal spectrum width W decreases in running-in friction process, maintains at small values in steady friction process, and increases rapidly in increasing friction process. Corresponding shuffled series was analyzed to distinguish that the multifractality of friction coefficient is due to the long-range correlation of the fluctuations. The results inform quantitative interpretations of friction system's tribological behavior and friction process identification.

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Multifractal Detrended Fluctuation Analysis of Temperature Reanalysis Data over Greece

Atmosphere ◽

10.3390/atmos10060336 ◽

2019 ◽

Vol 10 (6) ◽

pp. 336 ◽

Cited By ~ 6

Author(s):

Kostas Philippopoulos ◽

Nikolaos Kalamaras ◽

Chris G. Tzanis ◽

Despina Deligiorgi ◽

Ioannis Koutsogiannis

Keyword(s):

Time Series ◽

Spatial Distribution ◽

Detrended Fluctuation Analysis ◽

Multifractal Spectrum ◽

Spectral Width ◽

Reanalysis Data ◽

Fluctuation Analysis ◽

Spectral Parameters ◽

Multifractal Detrended Fluctuation Analysis ◽

Detrended Fluctuation

The Multifractal Detrended Fluctuation Analysis (MF-DFA) is used to examine the scaling behavior and the multifractal characteristics of the mean daily temperature time series of the ERA-Interim reanalysis data for a domain centered over Greece. The results showed that the time series from all grid points exhibit the same behavior: they have a positive long-term correlation and their multifractal structure is insensitive to local fluctuations with a large magnitude. Special emphasis was given to the spatial distribution of the main characteristics of the multifractal spectrum: the value of the Hölder exponent, the spectral width, the asymmetry, and the truncation type of the spectra. The most interesting finding is that the spatial distribution of almost all spectral parameters is decisively determined by the land–sea distribution. The results could be useful in climate research for examining the reproducibility of the nonlinear dynamics of reanalysis datasets and model outputs.

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