Cantilever beam forming machine multi-source data fusion control and intelligent monitoring method based on digital twinning

CN122447384APending Publication Date: 2026-07-24CHINA RAILWAY CONSTR BRIDGE ENG BUREAU GRP CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA RAILWAY CONSTR BRIDGE ENG BUREAU GRP CO LTD
Filing Date
2026-06-23
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

The cantilever beam-making machine has several problems during lifting, traveling, and attitude adjustment, including asynchronous operation of multiple hydraulic cylinders, difficulty in quantifying off-center load disturbances, significant nonlinearity of the hydraulic system, strong vibration and noise interference from the construction environment, and difficulty in early identification of abnormal signs.

Method used

A multi-source data fusion control and intelligent monitoring method for cantilever beam-making machines based on digital twins is adopted. By improving the extended Kalman filter, the joint estimation of cylinder displacement, pressure and unknown disturbance force is realized. The isolated forest is improved for anomaly detection. The estimation results are linked with the anomaly detection results for multi-cylinder load balancing, synchronous control and digital twin visualization monitoring.

Benefits of technology

It improves the state perception, anomaly identification and control coordination of the cantilever beam construction machine during construction, reduces missed detections, solves the problem of off-center load diffusion in the multi-cylinder synchronization process, and enhances the level and timeliness of construction safety management.

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Abstract

The application relates to the technical field of intelligent control of bridge construction, and discloses a cantilever beam builder multi-source data fusion control and intelligent monitoring method based on digital twinning, which comprises the following steps: collecting the displacement, pressure and construction vibration intensity of each oil cylinder in real time; adopting attenuated memory improved extended Kalman filtering with unknown input decoupling estimation to jointly estimate the oil cylinder displacement, speed, pressure and unknown disturbance force, and outputting a multi-dimensional fusion feature vector containing fusion state estimation value, unknown disturbance force estimation value, health index and displacement estimation standard deviation; adopting an improved isolated forest based on a segmented cumulative probability density function to perform abnormal scoring and early warning; performing multi-cylinder load balancing adjustment and PID synchronous control based on the unknown disturbance force estimation value; and mapping the estimation result, abnormal score and early warning level to a digital twinning platform in real time, driving a three-dimensional model to perform state synchronization and visualized monitoring. Therefore, the multi-oil-cylinder collaborative control precision and abnormal detection capability of the cantilever beam builder are improved.
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