Fault diagnosis method based on signed directed graph and data constitution

A technology of symbolic directed graph and data reconstruction, applied in the direction of electrical testing/monitoring, etc., can solve the problems of difficult detection, transfer, and failure to take into account the problem of fault propagation, and achieve the effect of good diagnosis effect.

Inactive Publication Date: 2014-04-09
SHANGHAI JIAO TONG UNIV
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But the biggest disadvantage of this method is that the contribution value is easy to transfer from one variable to other variables, that is, the variable with the largest contribution value may not be the root cause of

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  • Fault diagnosis method based on signed directed graph and data constitution
  • Fault diagnosis method based on signed directed graph and data constitution
  • Fault diagnosis method based on signed directed graph and data constitution

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[0027] A fault diagnosis method based on symbolic directed graph and data reconstruction. Normal data is collected for offline training as known state measurement data, that is, the preprocessed data is decomposed by PCA, and the control limit of SPE is obtained. . Then, according to the system flow diagram, the SDG model is established, and the parameters of each variable V-mask are determined after setting the false alarm rate, the false alarm rate, and the detection offset. Then collect the process data of the unknown state of the system in real time, as the measurement data of the unknown state, monitor the CUSUM and sample SPE of each variable. If the SPE exceeds the control limit, it means that the system has malfunctioned. Then, the effective node is determined through the CUSUM statistics. Search all possible compatible paths and reconstruct the data in all compatible path directions. The direction with the largest fault isolation index is the true fault propagation dir...

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Abstract

The invention discloses a fault diagnosis method based on a signed directed graph and data constitution. The fault diagnosis method includes the steps of firstly, collecting normal data to conduct offline training, decomposing pre-processed data through a PCA, and solving a control limit of an SPE; secondly, setting up an SDG model according to a system flow graph, and determining parameters of all variables V-mask after setting the missing alarm rate, the false alarm rate and the detection offset; thirdly, collecting process data of system unknown state in real time, monitoring a CUSUM of all the variables and the SPE of a sample, if the SPE exceeds the control limit, diagnosing that a fault happens to a system, determining an effective node through the CUSUM, and conducting reconstitution on data in all compatible path directions by searching for all possible compatible paths, wherein the direction with the largest fault isolation index is taken as a real fault propagation direction, the starting node in the direction is taken as a causal variable of the fault, and an event which causes the node to be abnormal is taken as the root cause of the fault.

Description

technical field [0001] The invention relates to the field of fault diagnosis systems for multivariable complex systems, in particular to a fault diagnosis method based on symbol directed graphs and data reconstruction. Background technique [0002] With the continuous development of industrial process control systems in the direction of large-scale, intelligent and complex, security issues have increasingly become one of the main concerns of everyone. As one of the core components of the process control system, fault detection and diagnosis (FDD) technology for dynamic systems was formed and developed to meet the needs of industrial systems for improving reliability and reducing accident risks. In the past few decades, the problem of fault diagnosis has been widely concerned by scholars at home and abroad, and various methods of fault detection and isolation have emerged. These methods can be generally divided into two categories: qualitative methods and quantitative analys...

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Application Information

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IPC IPC(8): G05B23/02
Inventor 王毓魏岩张峰华杨煜普
Owner SHANGHAI JIAO TONG UNIV
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