An industrial equipment fault early warning method and system based on multi-modal data fusion
By using a multimodal data fusion method, combining voltage signals, acoustic signals, and historical data, multiple node sets are constructed, which solves the problem of insufficient accuracy of single signal early warning methods, realizes accurate and timely early warning of partial discharge of industrial equipment, and improves the accuracy and anti-interference capability of equipment status monitoring.
Patent Information
- Application Number
- CN202610605481.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-06
- Publication Date
- 2026-07-17
AI Technical Summary
In existing technologies, partial discharge early warning methods based on a single signal are easily affected by noise and operating condition fluctuations, resulting in insufficient identification accuracy and making it difficult to achieve accurate and timely early warning of partial discharge in industrial equipment.
By combining voltage signals, acoustic signals, and historical operating data from industrial equipment, and using multimodal data fusion methods, a set of discharge activity nodes, an acoustic anomaly node set, and a healthy baseline node set are constructed to obtain multiple basic early warning values, thereby enabling early identification and quantitative early warning of partial discharge anomalies.
It improves the accuracy and anti-interference capability of industrial equipment operation status monitoring, enhances the timeliness of fault early warning, and ensures the safe and stable operation of equipment.
Smart Images
Figure CN122410230A_ABST