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, and even tend to be the same as the number of variables, making SPE (Squared Prediction Error, squared prediction error) ) statistics are almost invalid, so they cannot be used for actual monitoring

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