Dynamic process monitoring method based on weighted dynamic distributed PCA model

Abstract

The invention discloses a dynamic process monitoring method based on a weighted dynamic distributed PCA model. The method is to solve a problem how to effectively describe the dynamic characteristics of each measured variable for the complex dynamic characteristics of modern industrial process data and to establish a dynamic distributed monitoring model on this basis. The method weights each variable in a augmented matrix by using a correlation coefficient between each measured variable and other different delayed measurement values so that the weighted training data can better reflect the dynamic relation of the corresponding measured variable. The PCA model established on this basis can better excavate the hidden information related to each measured variable, and the interpretability of the model can be further improved. Compared with a traditional method, despite establishing a PCA fault detection model by using all different delay variables, the method assigns larger weights to the variables with large correlation and assigns smaller weights to the variables with small correlation, which not only prevents information loss to the utmost extent, but also highlights the process variables with strong correlation because of different weight values while suppressing the interference of irrelevant variables. Therefore, the dynamic process monitoring method based on a weighted dynamic distributed PCA model can obtain a superior fault detection effect.

Description

technical field [0001] The invention relates to an industrial process monitoring method, in particular to a dynamic process monitoring method based on a weighted dynamic distributed PCA model. Background technique [0002] Real-time monitoring of the operating status of the production process is a necessary means to ensure production safety and maintain stable product quality. Reliable and effective fault detection methods can be said to be an indispensable part of achieving this goal. In the past ten years, the modern industrial process has gradually completed the transformation from a single production unit to a combination of multiple interlaced production units. It is difficult to obtain a physical model that can accurately describe its operating mechanism, so the process monitoring method based on the mechanism model is no longer suitable for modern industrial processes. Instead, a data-driven approach to process monitoring centered on sampled data from the production ...

AI Technical Summary

Problems solved by technology

Although this can greatly eliminate the interference of irrelevant variables on modeling, it will cause some information to be lost

On the contrary, if a smaller threshold is set, although information loss is not a problem, irrelevant variables will introduce interference factors to the model

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