一种基于机器学习并考虑服役环境的发动机复合材料损伤状态评估方法及系统
By constructing a multi-source data association and machine learning model, the problem of not considering the service environment in the assessment of damage status of engine composite materials was solved, and the accurate assessment and classification of damage status were realized, improving the accuracy of the assessment and the ability to represent nonlinear relationships.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CIVIL AVIATION UNIV OF CHINA
- Filing Date
- 2026-04-13
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies fail to adequately consider actual service environment factors when assessing the damage status of engine composite materials, resulting in inaccurate damage status assessments. Furthermore, they do not make sufficient use of multi-source maintenance data and cannot effectively handle the impact of complex nonlinear damage.
By acquiring operational and maintenance information, service environment data, and other data on key composite components of the engine, we establish multi-source data associations, extract damage characteristic parameters, construct a damage status assessment index system, and use machine learning algorithms to build a damage status assessment model for quantitative assessment and classification.
It enables accurate assessment of the damage state of engine composite materials, improves the data integrity and accuracy of damage state assessment, has the ability to characterize nonlinear relationships, and supports maintenance decisions.
Smart Images
Figure CN122020396B_ABST