Non-Gaussian process monitoring method based on multi-variable block interleaving correlation elimination

Abstract

The invention discloses a non-Gaussian process monitoring method based on multi-variable block interleaving correlation elimination, and aims to take interleaving correlation into a distributed process modeling and process monitoring, thereby applying more reliable and effective distributed non-Gaussian process monitoring. The method comprises the steps of dividing all measuring variables into a plurality of variable sub-blocks according to ownership of the variable which is measured by each production unit; secondly, eliminating interleaving correlation information between each variable sub-block and other variable sub-block by means of a regression model; and finally, performing modeling based on an independent component analysis algorithm and non-Gaussian process monitoring by means ofthe error after interleaving correlation elimination. Compared with a traditional method, the method of the invention is mainly advantageous in that the regression model is utilized for taking the interleaving correlation between different multi-variable sub-blocks is considered, and the error after interleaving correlation elimination is used as a new monitoring object.

Description

technical field [0001] The invention relates to a data-driven process monitoring method, in particular to a non-Gaussian process monitoring method based on multivariable block cross-correlation elimination. Background technique [0002] Real-time monitoring of the operating status of the production process is of great significance to ensure production safety and product quality stability. The process monitoring technology usually adopted in production practice is the well-known fault monitoring method, which aims to identify abnormalities in the system in a timely manner. state. In recent years, with the advancement of large-scale industrialization and "big data" construction, a large amount of sample data can be easily collected during the production process, but it is impossible to establish an accurate mechanism model for it. In this application background, the traditional fault monitoring method based on the mechanism model has encountered a development bottleneck, whil...

AI Technical Summary

Problems solved by technology

Therefore, according to the method of dividing the production unit into variable sub-blocks, and then directly building multiple distributed process monitoring models, the cross-correlation between different production units cannot be taken into account.

Therefore, the traditional distributed non-Gaussian process monitoring method needs to be further improved

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