Multivariate Graph-Based Analysis on Coefficient of Friction Signal During the Friction Process

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Guodong Sun ◽  
Hua Zhu ◽  
Shihui Lang ◽  
Cong Ding
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Steady State ◽  
Tribological Behavior ◽  
State Transitions ◽  
Friction Process ◽  
Embedding Technique ◽  
Aisi 52100 ◽  
Aisi 1045 ◽  
Scalar Time Series

To describe the dynamic evolutionary law and tribological behavior of the tribopair AISI 52100-AISI 1045, rotational experiments were conducted by sliding a disk against a static pin. The multidimensional phase spaces were reconstructed based on the scalar time-series by the time-delay embedding technique, and the multivariate graph-based method was used to visualize the overall picture of the phase space. The evolution of radar plots and the corresponding multivariate graph centrobaric trajectory (MGCT) is consistent with the description of “running-in, steady-state and increasing friction stages,” and can serve as effective indicators for the friction state transitions. Results show that the radar plot can inform quantitative interpretations of friction process identification. Therefore, the multivariate graph-based method is a useful approach to characterize the nonlinear dynamics of tribological behaviors.

Multifractal Detrended Fluctuation Analysis on Friction Coefficient During the Friction Process

2017 ◽  
Vol 140 (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.

Using recurrence plots for stability analysis of ring-on-disc tribopairs

2019 ◽  
Vol 71 (4) ◽  
pp. 532-539
Author(s):  
Guodong Sun ◽  
Hua Zhu ◽  
Cong Ding
Keyword(s):  
Recurrence Plots ◽  
Friction Process ◽  
Content Type ◽  
Tribological Behaviors ◽  
Aisi 52100 ◽  
The Coefficient Of Friction ◽  
Aisi 1045 ◽  
Quantification Analysis ◽  
Evolution Laws ◽  
The Stability

Purpose AISI 52100-AISI 1045 specimens were used as the ring-on-disc tribopairs in the experiments to investigate the stability of friction process. Design/methodology/approach The coefficient of friction (COF) signals were measured throughout the friction process and the recurrence plots (RPs) and recurrence quantification analysis (RQA) are adapted to analyze the stability of the tribosystem. Findings The results show that the COF time-series acquired from different tests possess the same dynamic evolution laws. The evolution of RPs follows the rules of “disrupted-homogeneous-disrupted,” which corresponds to the “running-in, steady-state and increasing stages” of friction process. Additionally, the evolution laws of RQA measures LAM, Vmax and TT accord with the “bathtub curve.” Therefore, both RPs and RQA measures can inform quantitative interpretations of tribological behaviors and friction process identification. Originality/value The both RPs and RQA are capable of characterizing the tribological behaviors and can depict the various stages of friction process.

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

Phenomenological Modelling

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Real World ◽  
Biological Diversity ◽  
False Positives ◽  
World Systems ◽  
Economic Activities ◽  
Essential Services ◽  
Low Dimension ◽  
Convergent Cross Mapping

Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

Characterization of Epileptic EEG Time Series (I): Gabor Transform and Nonlinear Dynamics Methods

1997 ◽  
pp. 179-226 ◽  
Author(s):  
Susana Blanco ◽  
Silvia Kochen ◽  
Rodrigo Quian Quiroga ◽  
Luis Riquelme ◽  
Osvaldo A. Rosso ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Gabor Transform

scholarly journals Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 1013 ◽  
Author(s):  
David Cuesta-Frau ◽  
Antonio Molina-Picó ◽  
Borja Vargas ◽  
Paula González
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Classification Accuracy ◽  
Markov Models ◽  
Hidden Markov ◽  
Permutation Entropy ◽  
Time Series Classification ◽  
Discriminating Power ◽  
Ordinal Patterns ◽  
Temperature Records

Many measures to quantify the nonlinear dynamics of a time series are based on estimating the probability of certain features from their relative frequencies. Once a normalised histogram of events is computed, a single result is usually derived. This process can be broadly viewed as a nonlinear I R n mapping into I R , where n is the number of bins in the histogram. However, this mapping might entail a loss of information that could be critical for time series classification purposes. In this respect, the present study assessed such impact using permutation entropy (PE) and a diverse set of time series. We first devised a method of generating synthetic sequences of ordinal patterns using hidden Markov models. This way, it was possible to control the histogram distribution and quantify its influence on classification results. Next, real body temperature records are also used to illustrate the same phenomenon. The experiments results confirmed the improved classification accuracy achieved using raw histogram data instead of the PE final values. Thus, this study can provide a very valuable guidance for the improvement of the discriminating capability not only of PE, but of many similar histogram-based measures.

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