Health monitoring method for carbon fiber composite material structure

By collecting and analyzing the electrical conductivity data of carbon fiber composite materials in real time, and combining feature extraction and time series models, the density of sensing channels is adaptively adjusted, which solves the problem of real-time non-destructive damage detection and prediction of carbon fiber equipment, and improves the accuracy and intelligence level of monitoring.

CN122242243APending Publication Date: 2026-06-19DONGGUAN SUPER SPORTS EQUIP CO LTD
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Patent Information

Application Number
CN202610358866.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-23
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing carbon fiber composite materials equipment cannot accurately locate damage and predict damage propagation trends in real time and without damage at the sports field, and improper sensor placement may reduce structural reliability and increase costs.

Method used

By collecting real-time electrical conductivity data of carbon fiber composite materials, an initial resistance distribution map is generated. Noise is filtered out by combining a feature extraction model, frequency domain characteristics are analyzed to determine the damage type and quantify the severity, and a damage evolution trend map is generated by combining a time series model. The density of sensing channels is adaptively adjusted to optimize the configuration of the monitoring system.

Benefits of technology

It enables real-time, non-destructive, and precise damage detection and prediction of carbon fiber equipment, improving the accuracy and intelligence of monitoring, ensuring safe use, and extending service life.

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Abstract

The invention relates to a health monitoring method for a carbon fiber composite material structure in the field of carbon fiber composite materials, and the method comprises the steps: obtaining the conductive characteristic data of the composite material structure in a stress state, generating an initial resistance distribution diagram according to the conductive characteristic data, recognizing an abnormal region, and obtaining a preliminary signal set containing a potential damage position; extracting a spatial feature vector according to the preliminary signal set, and processing the spatial feature vector by adopting a feature extraction model to filter noise interference to obtain an enhanced signal sequence; processing the monitoring data of the multiple time nodes by adopting a time sequence fusion model, and generating a damage evolution trend chart reflecting a damage expansion trend; and based on the spatial distribution of signal peak points in the damage evolution trend chart, performing hotspot area analysis, and if the spatial dispersion of the peak points exceeds a preset range, adjusting the distribution density of sensing channels, and generating optimized monitoring system configuration parameters.
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