A noise-robust Multivariate Multiscale Permutation Entropy for two-phase flow characterisation

Autor:	Fabila-Carrasco, John Stewart, Tan, Chao, Escudero, Javier
Rok vydání:	2022
Předmět:	Physics - Data Analysis Statistics and Probability Mathematics - Combinatorics 05C76 76T10 94A17: 05C21
Druh dokumentu:	Working Paper
Popis:	Using a graph-based approach, we propose a multiscale permutation entropy to explore the complexity of multivariate time series over multiple time scales. This multivariate multiscale permutation entropy (MPEG) incorporates the interaction between channels by constructing an underlying graph for each coarse-grained time series and then applying the recent permutation entropy for graph signals. Given the challenge posed by noise in real-world data analysis, we investigate the robustness to noise of MPEG using synthetic time series and demonstrating better performance than similar multivariate entropy metrics. Two-phase flow data is an important industrial process characterised by complex, dynamic behaviour. MPEG characterises the flow behaviour transition of two-phase flow by incorporating information from different scales. The experimental results show that MPEG is sensitive to the dynamic of flow patterns, allowing us to distinguish between different flow patterns. Comment: 5 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.09030 Zobrazit plný text záznamu View this record from Arxiv