A method and system for predicting wear of a suspension clamp
By combining variational mode decomposition and Transformer model with finite element technology, a suspension clamp wear prediction model was constructed, which solved the problem of low wear monitoring accuracy in the existing technology, realized accurate prediction of the wear degree of suspension clamps, and ensured the safety of transmission lines.
CN120850665BActive Publication Date: 2026-06-09ZHANGJIAKOU POWER SUPPLY COMPANY OF STATE GRID JINBEI ELECTRIC POWER COMPANY +1
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHANGJIAKOU POWER SUPPLY COMPANY OF STATE GRID JINBEI ELECTRIC POWER COMPANY
- Filing Date
- 2025-07-10
- Publication Date
- 2026-06-09
AI Technical Summary
Technical Problem
In existing technologies, the wear monitoring of suspension clamps relies on finite element analysis, which leads to reduced accuracy, makes it impossible to accurately predict wear conditions, and poses safety hazards.
Method used
By acquiring real-time operational status data and meteorological data, and utilizing variational mode decomposition and Transformer models in conjunction with finite element technology, a wear prediction model is constructed to dynamically calculate the degree of wear.
Benefits of technology
It improves the accuracy of suspension clamp wear prediction, enabling timely detection of potential wear problems and ensuring the safe and stable operation of transmission lines.
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Smart Images
Figure CN120850665B_ABST
Abstract
The embodiment of the application discloses a wear prediction method and system of a suspension clamp, and relates to the technical field of electric power fittings. The precision of the wear prediction result of the suspension clamp can be effectively improved. The method comprises the following steps: acquiring working state data and real-time meteorological data of the suspension clamp; acquiring working state data and real-time meteorological data of a target suspension clamp; performing variational modal decomposition preprocessing on the working state data to obtain a modal function; and inputting the modal function and the real-time meteorological data into a wear prediction model based on a pre-trained Transformer model to predict the wear degree of the target suspension clamp. The application is suitable for a suspension clamp wear prediction scene.
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