Belt conveyor operation state prediction method and system based on digital twinning

By dynamically calculating the adaptive forgetting factor and load change characteristic compensation using digital twin technology, and optimizing the recursive least squares method, the problem of slow thermodynamic state tracking caused by the fixed forgetting factor is solved, and accurate state prediction and spare parts inventory management of the belt conveyor drive system are realized.

CN122288014APending Publication Date: 2026-06-26XIANYANG NORMAL UNIV +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XIANYANG NORMAL UNIV
Filing Date
2026-03-30
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

The recursive least squares algorithm with a fixed forgetting factor in the existing technology cannot flexibly adjust the forgetting rate of historical data, resulting in slow tracking of severe thermodynamic conditions, delayed prediction of thermal decay time, and easy to cause equipment downtime production accidents and improper spare parts inventory management.

Method used

A digital twin-based method for predicting the operating status of belt conveyors is adopted. By constructing a digital twin thermodynamic observation model, dynamically calculating the adaptive forgetting factor, and combining load change characteristic compensation correction, the covariance matrix and equivalent heat dissipation coefficient in the recursive least squares algorithm are optimized to achieve accurate prediction of the critical time of thermal damage.

Benefits of technology

This improves the algorithm's agility in the face of severe thermodynamic conditions and the robustness of parameter estimation, ensuring intelligent allocation of spare parts inventory and reducing equipment operating costs.

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Abstract

This invention discloses a method and system for predicting the operating status of a belt conveyor based on digital twins. The method includes: collecting operating and environmental data of the belt conveyor drive system; constructing a digital twin thermodynamic observation model based on lumped parameter thermal network theory; analyzing the model prediction residual characteristics to obtain an initial adaptive forgetting factor; combining load fluctuation characteristics with compensation and correction to obtain a final adaptive forgetting factor; updating the algorithm covariance matrix and equivalent heat dissipation coefficient; finally, using the optimized algorithm to deduce the trajectory of the equivalent heat dissipation coefficient; predicting the critical time for thermal damage through an extrapolation model; and automatically triggering spare parts inventory allocation when this time meets the warning conditions. This invention optimizes the adaptive forgetting factor mechanism of the recursive least squares method, improving the accuracy and robustness of status prediction, realizing intelligent collaboration between equipment operation and maintenance and spare parts scheduling, and reducing the operating cost of the equipment throughout its entire lifecycle.
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