Batch process failure monitoring method based on AR-PCA (Autoregressive Principal Component Analysis)

Abstract

The invention discloses a batch process failure monitoring method based on AR-PCA (Autoregressive Principal Component Analysis). Through the batch process failure monitoring method, the batch process with strong dynamics can be monitored online; when monitoring the bath process, a conventional MPCA (Multiway Principal Component Analysis) does not take corresponding self-correlation and mutual correlation of variables due to the existence of various random noises and interferences into account, so that a large quantity of false alarm is generated in the online monitoring process. The batch process failure monitoring method comprises the following steps: firstly, building a multi-variable autoregressive (AR) model according to measurement variables, recognizing a model coefficient matrix by using a PLS (Partial Least Squares) method and recognizing a model order by using an AIC (Akaike Information Criterion); and then building a PCA model for a residual error of the AR model. Meanwhile, training data is introduced when a new bath of data is monitored online through the algorithm, so that the monitoring effect of the algorithm is improved. Through the batch process failure monitoring method, the defect of a large quantity of false alarm of the conventional MPCA method during the process of monitoring the batch process with strong dynamics can be made up; and the batch process failure monitoring method is of great significance to monitoring of an actual bath production process.

Description

technical field [0001] The invention relates to a fault monitoring method for intermittent processes, especially for the penicillin fermentation process with relatively strong dynamics. The method is used to monitor the faults in the production process and detect faults in the production process in time. Background technique [0002] The process scale of modern process industry is constantly expanding, the complexity is increasing, and the investment is increasing, forcing people to pay more and more attention to the safety and reliability of process production; especially in some biological and chemical processes, which often contain high temperature and high pressure. , Inflammable and explosive production process, once an accident occurs in the system, it will cause huge losses of personnel and property, and the environmental pollution is much more serious than other accidents. However, although many production processes have been automated with the gradual popularization...

AI Technical Summary

Problems solved by technology

This method solves the problem of poor monitoring effect caused by the dynamic nature of process data, and improves the monitoring performance

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