Online updating method of principal component analysis monitoring model

Abstract

The invention relates to an online updating method of a principal component analysis monitoring model. The method comprises the following steps that: 1) A model online updating system comprising data acquisition equipment and a monitoring computer is arranged in industry field; 2) A traditional principal component analysis (PCA) modeling module uses historical data to establish a PCA initial monitoring model; 3) After the monitoring begins, a mean value variance updating module calculates a mean value and a standard deviation sigma' of a new model according to real-time process data and the current PCA model; 4) A projection point calculation module calculates a residual vector of a new sample and transmits to a residual determination module; 5) The residual determination module determines an updating method of a projection direction according to a size of a residual vector die; if the residual is large, a principal component space adjusting module is called; if the residual value is small, a principal component direction fine adjusting module is called; finally a load vector P' nk and a characteristic value matrix lambda' kk of the new model is obtained; 6) A control limit updating module carries out control limit and updating on statistical magnitude of the model; the system finally outputs the new model omega' which is used for online monitoring and fault diagnosis during an industrial process.

Description

technical field [0001] The invention relates to an online updating method for a multivariate statistical process monitoring model, in particular to an online updating method for a multivariable statistical process monitoring model based on an incremental principal component analysis method. Background technique [0002] PCA (Principal Components Analysis, Principal Component Analysis) is a multivariate statistical process monitoring modeling method. By mining the linear correlation between process variables, a monitoring model that reflects the internal laws of the system is established and provided to the multivariable statistical process monitoring system. , so as to realize the effective monitoring of the production process. The traditional PCA-based monitoring model modeling process first needs to collect a large amount of production data that can represent the characteristics of the production process, and then establish a monitoring model based on these historical data...

AI Technical Summary

Problems solved by technology

However, when this method is applied to multivariate statistical process monitoring, the time-varying nature of the data will lead to an increase in the number of pivots in the monitoring model, an

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