An interactive network modeling method for electromechanical systems based on adaptive symbolic transfer entropy

Abstract

The invention discloses an interactive network modeling method of complex electromechanical system in process industry based on adaptive symbol transfer entropy, the symbolic common parameters of timeseries are obtained on the basis of multivariate space reconstruction, the probability density and distribution of the original time series are estimated by using the adaptive kernel density estimation method, and divide the sequence into equal probability, by obtaining the best number of symbols and dividing intervals, coarse-grained symbolic representation of the original sequence is implemented, in order to improve the accuracy of the measurement of interaction information between variables, the symbolic sequence of monitoring variables is analyzed by transfer entropy, and the net information transfer quantity is calculated, so as to obtain the basic parameters needed for system interaction network modeling, and establish the network model reflecting the interaction mechanism of the actual system bottom layer. The network model will provide a basis for system state assessment, fault propagation analysis and diagnosis decision-making, so as to improve the scientific and intelligentdecision-making level of complex electromechanical systems in process industry under complex operating conditions.

Description

technical field [0001] The invention relates to the field of service safety state evaluation of complex electromechanical systems, in particular to a method for modeling an interactive network of electromechanical systems based on adaptive symbol transfer entropy. Background technique [0002] The process industry production system has a lot of production equipment and requires various auxiliary systems. The exchange of materials, information and energy is constantly carried out between the structural units. The internal correlation coupling degree of the system is high, and it is a distributed complex electromechanical system. Complex network is an important theory to study the structure, function and dynamic behavior of complex systems. Network modeling is an important means of complex system modeling, and it is also an active direction with the earliest research and the most achievements in the field of complex networks. Among many network modeling methods, it is a topic...

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Problems solved by technology

However, the original symbol transfer entropy is based on the symbolization principle of permutation entropy, and only uses the arrangement order of vector elements in the time series phase space as symbolization. This symbolization principle is a rough symbolization method, which may The structural information of the original time series is lost, resulting in the loss of information of the original sequence, which in turn affects the accurate measurement of the interactive information between variables

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