Process monitoring method and system based on slow feature analysis

Abstract

The present invention relates to a process monitoring method and system based on slow feature analysis, the method includes: reading the slow feature model and the monitoring model, reading in the online measurement value of the process variable, forming an input vector and inputting it into the slow feature model, and Obtain the online real-time estimated value of the slow feature vector s(t); the vector s composed of the first M slow features 1:M (t) input into the monitoring model to obtain T 2 and S 2 Instantaneous estimate of the statistic; compare t 2 and and S 2 and like Then the judgment process deviates from the steady-state operating point, if Then the determination process has a dynamic characteristic change. Through the technical scheme of the invention, not only the deviation of the steady-state operating point can be monitored, but also the dynamic characteristic change of the process can be effectively monitored.

Description

technical field [0001] The present invention relates to the technical field of process processing, in particular, to a process monitoring method based on slow feature analysis and a process monitoring system based on slow feature analysis. Background technique [0002] In industrial processes, faults, disturbances, and other anomalies seriously affect the normal operation of the process. In order to ensure the safe, stable and efficient operation of the production process, the real-time monitoring of the overall operation of the process and the timely detection of these abnormal conditions are of great significance. In recent years, the multivariate statistical process monitoring (MSPM) method has been paid much attention by scholars, and it has become a hot topic in the process control field, and is widely used in practical industrial production. Because the chemical process mechanism is complex, it is not easy to carry out theoretical analysis, and it is difficult to esta...

AI Technical Summary

Problems solved by technology

However, in this case, traditional process monitoring methods such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), etc., still give false alarm information by monitoring the shift of the steady-state operating point, and cannot Distinguish between normal working condition changes and disturbances and faults that really affect the process characteristics, causing the operators to fall into a large number of invalid false alarm information, which in turn causes the overall failure of the process monitoring module and increases the burden on the operators, affecting the operators to truly solve the problem. Attention to real safety hazards in the production process

[0004] When the steady-state operating point of the process is normally switched, the dynamic characteristics of the process have not changed substantially because the feedback control loop can still work normally; while an important feature of a real process failure is that the control loop cannot compensate for disturbances, The impact of faults, the dynamic characteristics of the process will be significantly different from the dynamic characteristics under normal working conditions, which will affect the smooth operation and stable control of production

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