风力机叶轮-齿轮箱复合故障的智能诊断系统及方法
By combining multimodal data acquisition with a feature-damage mapping model, the problem of accurate identification and visual tracing of composite faults in wind turbine impellers and gearboxes has been solved, improving the sensitivity and accuracy of fault diagnosis, extending equipment service life and reducing maintenance costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INNER MONGOLIA UNIV OF TECH
- Filing Date
- 2025-08-21
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies suffer from limited sensor types, lack of systematic deployment, and reliance on empirical rules for signal processing. This makes it difficult to effectively identify minor early-stage faults in key wind turbine components under multi-field coupling, affecting the accuracy of fault diagnosis and the safe and stable operation of wind power equipment.
By employing a multimodal data acquisition module, a tensor matrix is constructed and fault analysis is performed using a feature-damage mapping model. Combined with multi-source sensor deployment and cloud-edge collaboration technology, accurate identification and visual source tracing of composite faults in wind turbine impellers and gearboxes are achieved.
It improves the sensitivity and accuracy of fault diagnosis, enhances system response efficiency, extends equipment lifespan, and reduces maintenance costs.
Smart Images
Figure CN121047740B_ABST