Penicillin fermentation process fault monitoring method based on MLLE-OCSVM

Abstract

The invention discloses a penicillin fermentation process fault monitoring method based on MLLE-OCSVM, and relates to the technical field of the fault monitoring of the data drive. The method comprises two phases of off-line modeling and on-line monitoring. The off-line modeling comprises the following steps: firstly processing the three-dimensional data of the fermentation process; using a local linear embedding method (MLLE) in a manifold learning algorithm to execute the feature extraction to an original high dimensional data sample later; and finally using a one-class support vector machine (OCSVM) to execute the modeling construction monitoring statistics, and using a kernel density estimation method to determine the control limit. The on-line monitoring comprises the following steps: processing the newly-collected data according to the model, calculating the statistics and comparing with the control limit, and judging whether the fermentation process is run normally. The method does not need to assume that the fermentation process variable complies with the specific distribution of gauss or non-gauss, and the accuracy rate of the fault monitoring is higher.

Description

technical field [0001] The invention relates to the technical field of data-driven fault monitoring, in particular to a fault monitoring technology for batch processes. The data-driven method of the present invention is a specific application in fault monitoring of a typical batch process—penicillin fermentation process. Background technique [0002] Today, with the accelerated development of manufacturing technology, in order to meet various needs in the market, a large number of high-value-added products such as biochemical products, polymer products, and pharmaceutical products have emerged. Since the batch process can just meet the needs of producing high-value-added products , which has received more and more attention. In the batch process, the data obtained by people show more and more characteristics such as non-single working condition, nonlinearity, and non-Gaussian. At the same time, the dimensionality of the data obtained in the production process is getting hi...

AI Technical Summary

Problems solved by technology

However, the actual industrial process is mostly a mixed distribution of Gaussian and non-Gaussian, so the traditional multivariate statistical monitoring method is limited in application because it needs to assume that the process variable obeys a specific distribution

Some scholars have proposed a combined method MLLE-PCA to monitor non-Gaussian information and Gaussian information separately, but this method cannot effectively distinguish non-Gaussian information from Gaussian information

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