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

Abstract

The invention discloses a multi-working condition and multi-stage batch process monitoring method based on density peak clustering and real-time learning. The method combines density peak clustering and real-time learning algorithms to solve multi-mode and multi-stage batch process monitoring issues. In order to solve the problem of batch-to-batch variance and non-Gaussian distribution in batch process data, density peak clustering is firstly used to classify and identify the working conditions and stages of batch process data. Due to the diversity of quality variable trajectories under the same working conditions and stages, just-in-time learning is used to extract similar trajectories to obtain sub-datasets with similar quality variable trajectories. Therefore, for each quality variable trajectory for each subphase in a certain subcase, a submodel will be built to enable an accurate modeling and monitoring scheme. Finally, a Bayesian fusion method is introduced as an ensemble strategy to determine the final probability of failure. Compared with other existing methods, the method of 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-condition and multi-stage batch process monitoring method based on density peak clustering and real-time learning. Background technique [0002] In recent years, as an important part of process system engineering, process monitoring technology in industrial production 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 a 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 least sq...

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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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