Multi-working-condition multi-stage batch process monitoring method based on density peak clustering and instant learning

Abstract

The invention discloses a multi-working-condition multi-stage batch process monitoring method based on density peak clustering and instant learning, and the method is used for solving the problem of multi-mode multi-stage batch process monitoring by combining algorithms such as density peak clustering, instant learning and the like. In order to solve the problems of inter-batch difference and non-Gaussian distribution in batch process data, firstly, working conditions and stages of the batch process data are classified and identified by using density peak clustering; due to the fact that quality variable tracks under the same working condition and stage are diversified, similar tracks are extracted through instant learning so as to obtain sub-data sets with the similar quality variable tracks. Therefore, for each quality variable track of each sub-stage in a certain sub-working condition, a sub-model is established so as to realize an accurate modeling and monitoring scheme. Finally, aBayesian fusion method is introduced as an integration strategy to determine the final probability of the fault. Compared with other existing methods, the method provided by the invention has good effect and applicability.

Description

technical field [0001] The invention belongs to the field of industrial process control, and in particular relates to a multi-working-condition and multi-stage batch process monitoring method based on density peak clustering and real-time learning. Background technique [0002] In recent years, process monitoring technology in industrial production, as an important part of process system engineering, has attracted more and more attention from industry and academia. It plays a very important role in ensuring safe production and improving product quality, so it has very important research value. Traditional industrial engineering monitoring is generally based on process mechanism. Now, due to the development of distributed computer control system (DCS) technology, a large amount of process data has been collected, so the method based on data-driven multivariate statistical analysis has gradually attracted attention. Among them, principal component analysis (PCA) and partial l...

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Problems solved by technology

And the current process monitoring strategy is usually limited to single-condition multi-stage Gaussian batch process, and cannot deal with multi-condition multi-stage non-Gaussian batch process monitoring problems

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