System and method for production process self-adaption monitoring using OCSVM

Abstract

The invention relates to a system and method for production process self-adaption monitoring using an OCSVM. The system and method for production process self-adaption monitoring using the OCSVM is characterized in that the monitoring system comprises a data collecting module, a process monitoring module, an OCSVM model online update module and an alarming module; historical process variables and real-time process variables in an industrial production process are digitized through the data collecting module to historical process data and real-time process data and the historical process data and the real-time process data are transmitted to the process monitoring module; an original OCSVM monitoring model is established by the process monitoring module through the historical process data, the real-time process data are processed, so that an effective update sample is obtained, and the effective update sample and the OCSVM monitoring model are transmitted to the OCSVM model online update module together; after the OCSVM monitoring model is updated through the OCSVM model online update module, the OCSVM monitoring model is transmitted to the process monitoring module; when an abnormal sample is obtained by the process monitoring module, an alarm signal is generated and transmitted to the alarming module, so that an alarm is raised. The system and method for production process self-adaption monitoring using the OCSVM can be widely used for monitoring in the actual industrial production process.

Description

technical field [0001] The invention relates to an adaptive monitoring system and method of a production process, in particular to an adaptive monitoring system and method of a production process applying OCSVM (One-Class Support Vector Machine, single-class support vector machine). Background technique [0002] The multivariate statistical process monitoring method based on OCSVM has been widely and successfully applied in complex industrial process production. Based on the support vector machine theory proposed by Vapnik, this method separates the sample projection point in the feature space from the origin of the feature space with the largest interval in the form of a tangent plane, so that the normal samples where most samples in the original space are located are separated by the decision boundary. The distribution area is separated from the no-sample distribution area where anomalous samples may exist. The OCSVM monitoring model determines that new samples falling wi...

AI Technical Summary

Problems solved by technology

However, this requires a lot of inspection time and expensive inspection costs, making the speed of collecting effective update samples too slow, which will directly lead to too low update speed of the monitoring model, generating excessive false alarm signals, which may lead to failure of the monitoring model

Therefore, the method of offline testing of product quality in the laboratory still cannot provide new samples for the online update of the OCSVM model in a timely and effective manner.

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