A seamless steel tube blank blanking abnormal condition monitoring and control system

By obtaining the optimal combination of weight factors through particle swarm optimization algorithm, and combining seamless steel pipe billet specifications and current data sequences, the problem of poor identification of abnormal operating conditions in traditional monitoring methods is solved, and more efficient monitoring and control is achieved.

CN122125291APending Publication Date: 2026-06-02SHANDONG WANLI PRECISION MASCH MFG CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANDONG WANLI PRECISION MASCH MFG CO LTD
Filing Date
2026-04-08
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

In the production process of seamless steel pipe billet, existing technologies often fail to effectively identify or miss abnormal conditions due to traditional abnormal operating conditions, resulting in poor monitoring performance.

Method used

The initial weight factor combination is iterated using the particle swarm optimization algorithm. Combined with the billet specifications and current data sequences of historical seamless steel pipe samples, the optimal weight factor combination is obtained for the identification of abnormal operating conditions in the monitoring and control system.

Benefits of technology

It improves the ability to identify abnormal conditions during the seamless steel pipe billet blanking process, and enhances the accuracy and robustness of monitoring and control.

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Abstract

This invention relates to the field of working condition monitoring and control technology, specifically to a monitoring and control system for abnormal working conditions in seamless steel pipe billet cutting. The system includes a processor and a memory. The processor executes a computer program stored in the memory to perform the following steps: obtaining the fitness index value corresponding to the initial weight factor combination under a historical sample set of seamless steel pipes; iterating the initial weight factor combination using a particle swarm optimization algorithm; calculating the fitness index value corresponding to the new weight factor combination obtained in each iteration; stopping iteration when the fitness index value reaches an elbow inflection point; selecting the maximum fitness index value as the optimal index value under the historical sample set of seamless steel pipes, and the weight factor combination corresponding to the maximum fitness index value as the optimal weight factor combination; and monitoring and controlling the billet cutting production process based on the optimal index value and the optimal weight factor combination. Furthermore, this invention can improve the effectiveness of abnormal monitoring and control of the billet cutting production process.
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