Fermentation process fault monitoring method based on just-in-time learning local model

Abstract

The invention discloses a fermentation process fault monitoring method based on a just-in-time learning local model and relates to the field of data driving fault diagnosis. Global modeling can not overcome the problems of multiple stages of production process, seasonal effects and material quality effects during actual production, and a large number of false alarms will be produced during time-varying process. A local modeling method based on a just-in-time learning strategy is put forward to solve the problem of model mismatching during actual fault monitoring, and faults are monitored through a local partial least square model. Information entropy is introduced into the just-in-time learning strategy, and similar sample points are automatically selected for modeling. Due to the fact that the local model can represent the current system state, stage identification is not needed, the calculated amount is reduced, and problems which are brought to monitoring by time-variant characteristics in the fermentation process are overcome. The false alarm rate is effectively reduced, the fault monitoring accuracy rate is increased, and safety and economy of production are guaranteed.

Description

technical field [0001] The invention relates to the technical field of data-driven fault diagnosis, in particular to a fault monitoring technology for a multi-stage fermentation process. The data-driven method of the present invention is a specific application in fault monitoring of penicillin fermentation process. Background technique [0002] The process scale and complexity of modern fermentation industry are constantly expanding, and people pay more and more attention to the safety and reliability of process production. The process data of the process industry has problems such as high dimensionality, high coupling, collinearity, data loss, and noise pollution, while principal component analysis (PCA), principal component regression (PCR) and partial least squares (PLS) are the core technologies The multivariate statistical process monitoring (MSPM) method can better solve the above problems. Among them, partial least squares has been widely used because it can decompo...

AI Technical Summary

Problems solved by technology

[0003] However, the fermentation process often has no steady-state operating point, and often changes from one stable state to another, with dynamic and time-varying characteristics

The traditional partial least squares global modeling method will generate false alarms in strong dynamics such as transitional stages

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