Abnormality analysis method and system applied to hydrogel production control system

Through multiple prior abnormality diagnosis neural networks, the basic abnormality diagnosis neural network is guided and learned, and the target abnormality diagnosis neural network is generated, which solves the problems of inaccurate diagnosis and high missed diagnosis rate of abnormality analysis in the aqueous gel production control system, and achieves higher accuracy of abnormal state chain diagnosis prediction and stability of the production control system.

CN119645012AInactive Publication Date: 2025-03-18TAICHANG RESIN FOSHAN
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Patent Information

Application Number
CN202510175550.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-18
Publication Date
2025-03-18
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In the abnormal analysis of aqueous gel production control systems, it is difficult to adapt to the complex and variability of system operation data caused by changes in raw material characteristics, production process adjustments and fluctuations in environmental factors, resulting in inaccurate diagnosis and high missed diagnosis rate.

Method used

Multiple prior abnormality diagnosis neural networks with different abnormality diagnosis capabilities and initialized basic abnormality diagnosis neural networks are used for guidance and learning, and the target abnormality diagnosis neural network is generated to diagnose and predict the abnormal state chain of the system operation data of the aqueous gel production control system.

Benefits of technology

Through a gradual and distinct learning process, we can effectively explore abnormal characteristics at different levels, which improves the accuracy and comprehensiveness of the neural network for abnormal state chain diagnosis and prediction, and enhances the stability and reliability of the production control system.

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Abstract

The invention provides an anomaly analysis method and system applied to a hydrogel production control system, and the method comprises the steps: obtaining an initialized basic anomaly diagnosis neural network and X prior anomaly diagnosis neural networks with different anomaly diagnosis capabilities, and carrying out the abnormal state chain diagnosis prediction of system operation data, and performing guided learning on the initialized basic network by using a first prior anomaly diagnosis neural network with the weakest capability in the X prior anomaly diagnosis neural networks to generate a basic network after the first round of guided learning. And then utilizing an ith prior anomaly diagnosis neural network with a stronger anomaly diagnosis capability to continue guided learning on the basic network after the (i-1) th round of guided learning (i is an incremental integer greater than 1 and not greater than X), and finally outputting the basic network after the Xth round of guided learning as a target anomaly diagnosis neural network. The method is used for carrying out abnormal state chain diagnosis prediction on input system operation data, and the accuracy and reliability of abnormity analysis of the hydrogel production control system can be improved.
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