Monitoring index switching based multi-operating-mode process monitoring method and system

Abstract

The invention discloses a monitoring index switching based multi-operating-mode process monitoring method and system. The method includes: acquiring normal data in different operating modes to serve as a training sample set; obtaining a hidden Markov model on the basis of the training sample set, and acquiring control limits corresponding to monitoring indexes of the hidden Markov model; respectively establishing statistical pattern analysis models of corresponding operating modes on the basis of training samples of each operating mode, and acquiring control limits corresponding to monitoring indexes of each statistical pattern analysis model; computing operating mode vectors on the basis of process data acquired in real time, and further computing differential operating mode vectors; computing corresponding real-time monitoring indexes according to norms of the differential operating mode vectors, and comparing the real-time monitoring indexes with the control limits corresponding to the monitoring indexes of the corresponding models so as to monitor running states of the operating modes. The method has the advantages that the process data are acquired in real time, reliability in monitoring is guaranteed, data in each operating mode need not to obey Gaussian distribution, and applicability is higher.

Description

technical field [0001] The invention relates to the field of process monitoring, in particular to a multi-working-condition process monitoring method and system based on monitoring index switching. Background technique [0002] For process monitoring and fault diagnosis, traditional methods mostly use multivariable statistical process control (MSPC), in which principal component analysis (PCA) and partial least squares (Partial Least Squares, PLS) ) as the representative method has been successfully applied in industrial process monitoring. The traditional MSPC method assumes that the process runs under a single operating condition, but in fact, due to product changes, capacity adjustments, etc., the process often switches frequently among multiple operating conditions. [0003] Multi-condition process monitoring methods based on principal component analysis and support vector data description all assume that the data of each working condition obeys a Gaussian distribution,...

AI Technical Summary

Problems solved by technology

And although it is simpler to establish a unified model for multiple working conditions than the method of building multiple models, it lacks real-time identification of working conditions, which will make it impossible to monitor the working conditions of the current equipment

Although the multi-working condition process monitoring method based on sparse representation does not assume the Gaussian nature of the data, this method does not consider the dynamic characteristics of the chemical process

In addition, it may not be accurate to judge that the working condition of a single current data is affected by noise

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