Multivariable causal-driven complex electromechanical system service security situation assessment method

A technology of electromechanical system and security situation, which is applied in the field of complex electromechanical system state assessment to achieve the effect of good quantitative representation ability

Active Publication Date: 2020-04-14
XI AN JIAOTONG UNIV
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Problems solved by technology

[0003] The purpose of the present invention is to provide a multi-variable causal-driven complex electromechanical system service safety situation assessment method to overcome the curre...

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  • Multivariable causal-driven complex electromechanical system service security situation assessment method
  • Multivariable causal-driven complex electromechanical system service security situation assessment method
  • Multivariable causal-driven complex electromechanical system service security situation assessment method

Examples

Experimental program
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Embodiment

[0102] Example Select the 20 compressor unit monitoring variables shown in Table 1 to analyze the state monitoring data of 13 before a fault occurs. The sampling frequency of the monitoring data is 1 / 60HZ. The specific steps are as follows:

[0103] Step 1: Monitoring data acquisition and preprocessing

[0104] Obtain the state monitoring data of a compressor unit in a chemical enterprise 13 days before a fault occurs, and filter the state monitoring data to obtain the state monitoring data of 20 variables shown in Table 1. Since the data extracted directly from the factory DCS will inevitably be disturbed by the noise in the production process, before the multivariate causal measure analysis, the wavelet noise reduction method is used to denoise the multi-state monitoring data.

[0105] Step 2: Multivariate Causality-Driven System Network Modeling

[0106] The GPDC method is used to analyze the causal measure between variables on the preprocessed condition monitoring data, a...

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Abstract

The invention discloses a multivariable causal-driven complex electromechanical system service security situation assessment method, and belongs to the field of complex electromechanical system service process state evaluation. The method comprises the following steps: firstly, extracting state monitoring data in a complex electromechanical system service process, preprocessing the state monitoring data, calculating a causal measure value between monitoring variables by using a GPDC method, and constructing a causal network topology model capable of reflecting a system state evolution process;extracting features of three dimensions of an average path length, a clustering coefficient and a network structure entropy based on the established causal network model; and reconstructing an abnormal state space of the system according to the extracted abnormal fluctuation information of the three features, normalizing abnormal over-limit indexes on the three dimensions, and effectively evaluating the service state of the complex electromechanical system by using the normalized abnormal indexes.

Description

technical field [0001] The invention relates to the field of state evaluation of complex electromechanical systems, in particular to a multivariable causal-driven complex electromechanical system service safety situation evaluation method. Background technique [0002] The production process of the process industry is complex, and the components are highly correlated and coupled, which is a typical complex electromechanical system. In order to ensure the normal operation of the process industry production system, a large number of industrial instruments and sensors are usually installed in the system to monitor and control the operating status of the system in real time. These monitoring data contain rich status and working condition information of the process industry production system , which can be used to evaluate the service status of the system. However, the complex electromechanical system has a large scale, and the traditional state assessment method based on a sing...

Claims

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Application Information

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IPC IPC(8): G06F17/18
CPCG06F17/18
Inventor 谢军太高建民高智勇陈琨王荣喜王伟
Owner XI AN JIAOTONG UNIV
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