Industrial process monitoring method based on missing variable PCA model

Abstract

The invention discloses an industrial process monitoring method based on a missing variable PCA model, so as to use a missing variable processing method to online estimate principal component information and realize the purpose of executing monitoring on estimation errors. Firstly, the principal component is estimated by assuming that each measurement variable data is missing one by one; and then, with the estimation error of the principal component and the error estimation value of the PCA model as monitored objects, online process monitoring is executed. Although sampling data under a normal working condition do not necessarily satisfy the Gauss distribution hypothesis, the estimation errors generally obey the Gauss distribution. From the point of view, although the method is based on the PCA algorithm, the method does not require hypothetical training data to obey or approximately obey the Gauss distribution, and the application range of the traditional PCA-based process monitoring method is extended to a certain extent. Besides, the method also has the advantage of a high multi-model generalization capability as multiple fault detection models are adopted.

Description

technical field [0001] The invention relates to an industrial process monitoring method, in particular to an industrial process monitoring method based on missing variable PCA model. Background technique [0002] With the wide application of computer technology in the production of industrial processes, industrial objects can measure more and more indicators, and can also store massive process data information. The rich sampling data provides a solid data foundation for the modern industrial process to move towards the "big data" era, and has spawned many data-driven industrial informatization applications and research fields. Data-driven industrial process monitoring is one of the very important branches, which aims to realize the purpose of real-time monitoring of production process failure by mining useful information hidden in process data. In recent years, the research on data-driven process monitoring, especially the process monitoring based on multivariate statistica...

AI Technical Summary

Problems solved by technology

That is to say, at present, in the process monitoring model based on PCA, the missing variable processing method is usually only regarded as an "emergency" backup strategy, and the missing variable processing method has not yet fully exerted its effect in the PCA fault detection model.

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