Multi-phase batch process phase dividing method based on multiway principal component analysis method

Abstract

The invention relates to a multi-phase batch process phase dividing method based on a multiway principal component analysis method, which comprises the following steps: model data acquisition, three dimensional data expansion, two dimension matrix standardization, PCA decomposition, principal component number selection, load matrix decomposition and K-means cluster analysis. The method firstly uses a load matrix of a multiway principal component analytical method model to identify process phase according to timeslice sequence through decomposition deformation, thereby the misunderstanding that the multiway principal component analytical method is not suitable for the multi-phase batch process modeling in the traditional theory is solved. The method not only extracts cross correlation of variable on each timeslice, and simultaneously extracts the self dynamic change characteristic of measure variable in the whole batch at each sampling time for identifying the phase, thereby improving the monitoring efficiency during monitoring process and the accuracy of fault diagnosis result, and providing a new possibility for multi-phase process phase dividing under the condition without process prior knowledge.

Description

technical field [0001] The invention relates to a multi-stage batch process phase division method based on a multi-directional principal component analysis method, and belongs to the field of multi-variable monitoring and fault monitoring of intermittent processes. Background technique [0002] The correlation of each operating variable in the batch process operation does not change with time, but changes with the process operation process or the change of the process mechanism characteristics. Multi-stage is a prominent feature of the batch process. Understand the characteristics of each stage of the batch process The variable relationship helps to improve the monitoring efficiency of the gap process and enhance the reliability of diagnosis. [0003] At present, there are mainly three methods for dividing the multivariable process stages of a batch process with stage characteristics. The first method is to rely on the experience of process experts to identify the stages, bu...

AI Technical Summary

Problems solved by technology

[0003] At present, there are mainly three methods for dividing the multivariable process stages of a batch process with stage characteristics. The first method is to rely on the experience of process experts to identify the stages, but obviously, this method is too dependent on people, and the judgment results are too Subjective; the second method is to judge by the abnormal point of the track of a certain key process variable, but this method does not consider the correlation of process variables, and the stage identification is relatively rough; the third method is the Sub-PCA method, which uses The time slice matrix modeling of the extraction process is clustered and staged according to the load matrix of each time slice after modeling, but this method is the correlation information of each time slice matrix in the extraction process, ignoring each time slice The dynamic change information of the entire process of the matrix, the stage division results are relatively one-sided

Images