Chemical fault monitoring method based on generalized multi-block independent element analysis model

Abstract

The invention discloses a chemical fault monitoring method based on a generalized multi-block independent element analysis model, and aims to provide a generalized multi-block independent element analysis algorithm, and non-Gaussian multi-block modeling can be implemented for overlapped and non-overlapped variable sub-block division, so that the distributed fault monitoring is implemented by utilizing the generalized multi-block independent element analysis algorithm. According to the method, in the modeling process based on the independent meta-analysis algorithm, the uniqueness of each sub-block and the integrity between the sub-blocks are considered at the same time, and a mutually-staggered one-by-one extraction strategy from overall separation to local sub-block extraction and from local sub-block return to overall separation is adopted. Therefore, the method is a brand new non-Gaussian multi-block modeling and fault monitoring method. In addition, the superiority of the method provided by the invention can be verified by the specific embodiment, so that the method provided by the invention is a more optimal non-Gaussian distributed fault monitoring method.

Description

technical field [0001] The invention relates to a data-driven fault monitoring method, in particular to a chemical fault monitoring method based on a generalized multi-block independent element analysis model. Background technique [0002] Due to the wide application of computer technology in chemical industry production, process objects can be stored offline and measure massive amounts of data online, and modern industrial processes are gradually moving towards digital management. These data contain potential information that can reflect the operating status of the production process, and the use of sampling data to monitor the operating status of the process has been favored by many scholars. In the past ten years, both academia and industry have invested a lot of manpower and material resources in the research of fault monitoring technology. In the field of data-driven fault monitoring research, statistical process monitoring is the most studied method, among which Princ...

AI Technical Summary

Problems solved by technology

How to implement multi-block ICA modeling for this multi-block division result, and consider the interaction between blocks, has not yet been fully considered in literature or patents

The traditional multi-block principal component analysis algorithm also requires that the division of variable blocks does not overlap. Therefore, directly expanding multi-block principal component analysis into multi-block ICA algorithm cannot deal with the overlapping variable sub-block division.

Images