Method for monitoring and early warning abnormal working conditions in precision forming process of cartridge case

By combining DDFCM and DiPCA technologies, data fuzzy clustering and dynamic modeling are performed on the cartridge production process, which solves the problems of hysteresis effect and dynamic correlation of process parameters in the cartridge production process, realizes high-sensitivity and high-accuracy abnormality monitoring, and ensures production safety.

CN120656305APending Publication Date: 2025-09-16SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI +1
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Patent Information

Application Number
CN202510647466.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-20
Publication Date
2025-09-16

AI Technical Summary

Technical Problem

Existing technologies make it difficult to capture the hysteresis effects and dynamic correlations of process parameters in the cartridge production process in real time, and a single global model cannot adapt to changes in data distribution under different working conditions, resulting in insufficient sensitivity in abnormal monitoring and false alarms and missed detections.

Method used

Combining the diffusion distance fuzzy clustering method (DDFCM) and dynamic internal principal component analysis (DiPCA) technology, the cartridge production data is preprocessed and fuzzy clustered, a dynamic monitoring model is constructed, dynamic latent variables are extracted, and abnormality monitoring is achieved by calculating statistics.

Benefits of technology

It improves the real-time and accuracy of abnormal monitoring in the cartridge production process, can adapt to multiple working conditions, reduce data dimensions and retain key dynamic characteristics, and enhances the robustness and stability of the monitoring system.

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Abstract

The invention provides a cartridge case precision forming process abnormal working condition monitoring and early warning method. Diffusion distance fuzzy clustering (DDFCM) and dynamic internal principal component analysis (DiPCA) technologies are fused. The method comprises the following steps: firstly, performing fuzzy clustering on process data based on a diffusion distance by adopting a DDFCM method, automatically identifying key sample groups under different working conditions, and constructing a representative local training data set; thirdly, for each clustering sample group, constructing a dynamic monitoring model by using a DiPCA technology, effectively extracting potential variables containing time lag features, and capturing a dynamic change mode of a process; in a new data acquisition stage, firstly, the membership degree of a new sample in each cluster is calculated through DDFCM, and the new sample is distributed to a corresponding local DiPCA model for state discrimination according to a maximum membership degree principle. According to the method, the time sequence dynamic change in the process can be fully captured, the local characteristics in different production states are also considered, and a powerful guarantee is provided for the production stability and the product quality of the cartridge case coating, rolling and cutting process.
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