Dynamic process monitoring method based on nonlinear autocorrelation rejection of variables

Abstract

The invention discloses a dynamic process monitoring method based on nonlinear autocorrelation rejection of variables, and aims to reject nonlinear autocorrelation of each measured variable of sampledata and establish a nonlinear dynamic process monitoring model on the basis. The method comprises steps as follows: firstly, a nonlinear regression model between the sample data and multiple delay measurement data of the sample data with a kernel partial least-squares-algorithm; secondly, a model error is taken as a new monitored object, the process monitoring model is established with a PCA (principal component analysis) algorithm, and fault monitoring is implemented. Compared with a traditional method, the method has the advantages that the error is taken as the monitored object, the error's capacity of reflecting the abnormal change condition of nonlinear autocorrelation characteristics is utilized, furthermore, by means of absence of time sequence autocorrelation of error data, convenience is provided for following establishment of the process monitoring model based on the PCA algorithm. Therefore, the method is more suitable for the dynamic process monitoring.

Description

technical field [0001] The invention relates to a data-driven process monitoring method, in particular to a dynamic process monitoring method based on variable nonlinear autocorrelation elimination. Background technique [0002] The purpose of process monitoring is to find faults timely and accurately, which is of great significance to ensure safe production and maintain stable product quality. At present, the mainstream implementation technology of process monitoring is data-driven method, which is mainly due to the large-scale construction of modern chemical process and the wide application of advanced instrumentation and computer technology. Massive data can be collected in the production process. Due to the development of advanced instrument technology, the sampling time interval has been greatly shortened, and the time series autocorrelation between sampling data is a problem that must be considered in data-driven process monitoring methods. The most typical method in ...

AI Technical Summary

Problems solved by technology

In the current scientific research literature and patent documents, there are few dynamic process monitoring methods that consider the nonlinear autocorrelation of variables. How to eliminate this nonlinear autocorrelation feature urgently needs further in-depth research.

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