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Assessment method for coupling state of process industrial electromechanical system based on network structure entropy

A network structure and electromechanical system technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as lack of research methods, redundant monitoring information, and insufficiency, so as to improve the level of safe service and achieve refinement The effect of controlling and reducing computational complexity

Active Publication Date: 2018-11-06
XI AN JIAOTONG UNIV
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  • Application Information

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Problems solved by technology

These studies focus on different specific objects such as transformers, and have achieved good research results. However, for distributed complex electromechanical systems, the working conditions are complex, there are many monitoring variables, the coupling relationship is complicated, and there are redundant and insufficient monitoring information. objects, research methods are still relatively scarce

Method used

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  • Assessment method for coupling state of process industrial electromechanical system based on network structure entropy
  • Assessment method for coupling state of process industrial electromechanical system based on network structure entropy
  • Assessment method for coupling state of process industrial electromechanical system based on network structure entropy

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Experimental program
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Embodiment

[0061] A complex electromechanical system including n elements samples the required production elements according to a specific time period and records them in system variables. After m sampling periods, the system has a total of m×n system variable data:

[0062] Step 1: Monitoring dataset preprocessing

[0063] First of all, the heterogeneous and heterogeneous data should be normalized, and each value in the variable X should be processed as follows:

[0064] X(i)=X(i) / mean(X)

[0065] Noise reduction processing is performed on the normalized data, and the method of wavelet packet noise reduction is adopted here.

[0066] Step 2: Selection of time window width

[0067] The quasi-period of the time series is analyzed by the algorithm to represent the change period of the time series. That is, when the length of the sequence is greater than the quasi-period of the variable, this time series can better reflect the characteristics of the variable.

[0068] Here we use the FF...

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Abstract

The invention discloses an assessment method for the coupling state of a process industrial electromechanical system based on network structure entropy. According to the method, quasi periodicity of asequence is solved through an FFT method first to determine time window width for coupling analysis, a DCCA algorithm is utilized to calculate the relevancy between every two of multiple variables, aweighting network model reflecting the coupling relation among the multiple variables is constructed, therefore, changes of the coupling relation among process monitoring variables can be acquired intime, quick and precise scheduling on upstream and downstream parts of the system is realized, and fine control of the system is realized; an NSEn method is used to calculate an entropy value of a monitoring variable coupling relation network model in each time window, and the dynamic coupling process of different parts of the system can be visually reflected through dynamic changes of a networktopological structure according to the coupling incidence relation of monitoring data; and the network structure entropy is used for quantitatively representing the state evolution process of the system to provide comprehensive scheduling and maintenance decision information for system management personnel, and the scientific and intelligent level of a decision for secure and reliable operation ofthe process industrial complicated electromechanical system under complicated conditions is raised.

Description

technical field [0001] The invention relates to the field of service safety state evaluation of complex electromechanical systems, in particular to a DCCA-NSEn-based coupling network modeling and evaluation method for process industry electromechanical systems. 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. Equipment failures and process adjustments often lead to systematic fluctuations. Timely and accurate detection of operational failures in industrial processes and reasonable evaluation of the recovery degree of the failure process are particularly important for the reasonable regulation of the upstream and downstream of the process system. Dispatchers d...

Claims

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 高智勇谢军太高建民姜洪权王荣喜冯龙飞
Owner XI AN JIAOTONG UNIV
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