Fault diagnosis method based on signed directed graph and data constitution

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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Problems solved by technology

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 the failure

In addition, traditional multivariate statistical methods based on contribution graphs do not take into account the propagation of faults in the system, making it difficult to detect the root cause of faults

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