Next Article in Journal
Alternative Hydrophobic Core in Proteins—The Effect of Specific Synergy
Previous Article in Journal
An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons
Open AccessArticle

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

1
Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
2
Marine Engineering College, Dalian Maritime University, Dalian 116026, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 272; https://doi.org/10.3390/sym12020272 (registering DOI)
Received: 7 January 2020 / Revised: 5 February 2020 / Accepted: 7 February 2020 / Published: 11 February 2020
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.
Keywords: frictional vibration; multifractal detrended fluctuation analysis; spectrum parameter; feature extraction; friction state recognition frictional vibration; multifractal detrended fluctuation analysis; spectrum parameter; feature extraction; friction state recognition
MDPI and ACS Style

Li, J.-M.; Wei, H.-J.; Wei, L.-D.; Zhou, D.-P.; Qiu, Y. Extraction of Frictional Vibration Features with Multifractal Detrended Fluctuation Analysis and Friction State Recognition. Symmetry 2020, 12, 272.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop