Dispersion heterogeneous recurrence analysis and its use on fault detection

Recurrence plot is an effective tool for portraying system dynamics. However, dealing with distance matrices through the Heaviside function which is difficult to determine the threshold may lead to the loss of much important information, such as information about transitions between states. Therefore, in this paper, we propose a novel disper-sion heterogeneous recurrence analysis for complex systems to explore their intrinsic characteristics and structure. The use of dispersion patterns in symbolic dynamics can retain valuable information better than the original defined recurrence plots and avoid the challenge of choosing a threshold. Moreover, we use the iterated function system to provide a visual display of the transition information between patterns. Finally, attention entropy is used to develop a dispersion heterogeneous recurrence quantification analysis. Experimental results show that the method is able to detect the changes of system characteristics with parameters. Also, it can be combined with clustering and classification algorithms for fault detection of railway vehicle systems. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a dispersion heterogeneous recurrence analysis method for exploring the intrinsic characteristics and structure of complex systems. The method retains valuable information better than traditional recurrence plots and avoids the challenge of choosing a threshold. Experimental results demonstrate the ability of the method to detect changes in system characteristics and its potential for fault detection in railway vehicle systems.