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

An electromechanical system and network modeling technology, applied in transmission systems, digital transmission systems, data exchange networks, etc., can solve problems such as loss of original sequence information, accurate measurement of interaction information between influencing variables, loss of original time series structure information, etc. , to achieve the effect of improving accuracy and efficiency and simplifying the complexity of probability calculation

CN109088770AActive Publication Date: 2018-12-25XI AN JIAOTONG UNIV
9 Cites 3 Cited by

Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2018-12-25

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

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.
Need to check novelty before this filing date? Find Prior Art

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...

Examples

Embodiment Construction

[0048] The present invention is described in further detail below in conjunction with accompanying drawing:

[0049] Such as Figure 13 As shown in the present invention, an electromechanical system interaction network modeling method based on adaptive symbolic transfer entropy obtains the public parameters of the original time series symbolization on the basis of multivariate space reconstruction, and uses the adaptive kernel density estimation method to Estimate the probability density and probability distribution of the original time series, and divide the original time series with equal probability according to the principle of equal probability division. It is preferable to select the best number of symbols and division intervals, and perform coarse-grained symbol representation on the original time series to improve the accuracy of the measurement of mutual information between variables. On this basis, the original time series (for each pair of monitoring variables) The...