Exploring recursive properties and dynamical complexity in scalar time-series using threshold-free recursive analysis approach
Source abstract
Abstract
In this project, a threshold-free recursive analysis approach was investigated to reveal the recursive properties of dynamic systems. Specifically, the occurrence of recurrent patterns in phase trajectory was identified by a threshold-free recursive matrix whose elements would possess continuous values within the interval of [0, 1] and are inversely proportional to the distance between phase points. Subsequently, mapping and feature extraction were carried out to visualize and quantify the threshold-free recursive matrix. Theoretical time-series, along with observed time-series in the fields of engineering, finance, and hydrology, have been utilized to validate the efficacy of this approach. The results demonstrate the applicability of this method in analyzing the dynamic characteristics of scalar time-series. Furthermore, the rebuilt grayscale recurrence plot morphology and extracted measures through grayscale recurrence quantification analysis measures can effectively unveil the dynamical complexity of nonlinear systems by offering more precise analysis outcomes for investigating the evolution of recursive characteristics.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Complex Systems and Time Series Analysis · Chaos control and synchronization · Nonlinear Dynamics and Pattern Formation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Yangzhou University , Yangzhou 225127, China
Funding
National Natural Science Foundation of China · Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions
References (29)
Show source-provided references
1. A review of recent advances and applications of machine learning in tribology10.1039/d2cp03692d ↗
2. Triboinformatic modeling of dry friction and wear of aluminum base alloys using machine learning algorithms10.1016/j.triboint.2021.107065 ↗
3. Experimental investigation on identifying friction state in lubricated tribosystem based on friction-induced vibration signals10.1016/j.ymssp.2019.106590 ↗
4. Analysis of multi-stage running-in process of Sn–11Sb–6Cu alloy and AISI 1045 with phase trajectory plot10.1115/1.4052217 ↗
5. Research on diagnosis algorithm of mechanical equipment brake friction fault based on MCNN-SVM10.1016/j.measurement.2021.110065 ↗
6. Study on the boundedness, stability and dynamic characteristics of friction system based on fractal and chaotic theory10.1016/j.triboint.2023.108228 ↗
7. The nonlinear nature of friction coefficient in lubricated sliding friction10.1016/j.triboint.2015.02.027 ↗
8. Application of recurrence dynamic analysis to running-in state recognition10.1108/ilt-12-2020-0481 ↗
9. Recurrence plots of dynamical systems10.1209/0295-5075/4/9/004 ↗
10. Recurrence plots for the analysis of complex systems10.1016/j.physrep.2006.11.001 ↗
11. Transformer vibration feature extraction method based on recursive graph quantitative analysis
12. On a topological criterion to select a recurrence threshold10.1063/1.5116766 ↗
13. Trends in recurrence analysis of dynamical systems10.1140/epjs/s11734-022-00739-8 ↗
14. A variable threshold for recurrence based on local attractor density10.1063/5.0114797 ↗
15. A bottom-up approach for recurrence detection based on sampling distance10.1063/5.0160832 ↗
16. A novel tool for the identification and characterization of repetitive patterns in high-density contact mapping of atrial fibrillation10.3389/fphys.2020.570118 ↗
17. Alzheimer’s disease analysis algorithm based on no-threshold recurrence plot convolution network10.3389/fnagi.2022.888577 ↗
18. Non-intrusive load monitoring based on swin-transformer with adaptive scaling recurrence plot10.3390/en15207800 ↗
19. Reliability of recurrence quantification analysis of postural sway data. A comparison of two methods to determine recurrence thresholds10.1016/j.jbiomech.2020.109793 ↗
20. Recurrence measures and transitions in stock market dynamics10.1016/j.physa.2022.128240 ↗
21. Radius selection using kernel density estimation for the computation of nonlinear measures10.1063/5.0055797 ↗
22. Open-Set events identification based on deep metric-learning for DMZI perimeter system10.1109/jsen.2020.3006400 ↗
23. Learning dynamical systems in noise using convolutional neural networks10.1063/5.0009326 ↗
24. Classification of chaotic squeak and rattle vibrations by CNN using recurrence pattern10.3390/s21238054 ↗
25. Application of unthresholded recurrence plots and texture analysis for industrial loops with faulty valves10.1007/s00500-022-06894-3 ↗
26. Structural damage identification under nonstationary excitations through recurrence plot and multi-label convolutional neural network10.1016/j.measurement.2021.110101 ↗
27. Comparison of vibration visualization methods for classification of chaos based on CNN10.1007/s12206-022-0508-9 ↗
28. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings10.1109/tr.2018.2882682 ↗
29. Recurrence quantification based Liapunov exponents for monitoring divergence in experimental data10.1016/s0375-9601(02)00436-x ↗