飞行器结构壁板声疲劳损伤智能识别技术研究
Intelligent Identification Technology for Acoustic Fatigue Damage of Aircraft Structural Wall Panels
兴效鸣 1陈国一 1陈忠明2
作者信息
- 1. 天津航天瑞莱科技有限公司沈阳分部,沈阳 110000
- 2. 沈阳飞机设计研究所,沈阳 110000
- 折叠
摘要
目的 针对飞行器结构壁板损伤疲劳问题进行研究,提高对声疲劳损伤的智能识别能力.方法 构建一种基于梯度提升决策树(GBDT)的高效智能识别模型,用于识别飞行器结构壁板的声疲劳损伤.该方法依赖于GBDT模型的强大性能,能够有效处理复杂的非线性关系,并通过迭代学习不断优化识别结果.基于某复合材料结构壁板噪声实测数据,构建时间、速度、标签数据集进行模型验证.结果 基于GBDT的噪声疲劳损伤智能识别准确率为 76.8%.结论 基于GBDT的声疲劳损伤智能识别方法具有良好的识别能力,能够在实际应用中对飞行器结构壁板的声疲劳损伤进行有效监测,验证了该方法的有效性和实用性.
Abstract
The work aims to study the damage fatigue problem of aircraft structure panels,and improve the intelligent rec-ognition ability of acoustic fatigue damage.In this paper,an efficient intelligent recognition model based on gradient boosting decision tree(GBDT)was constructed to identify the acoustic fatigue damage of aircraft structural panels.This method relies on the powerful performance of the GBDT model.It can effectively deal with complex nonlinear relationships,and continuously optimize the recognition results through iterative learning.In the experiment,based on the measured noise data of a composite structure panel,the time,speed and label data sets were constructed for model verification.The verification results showed that the intelligent recognition accuracy of noise fatigue damage based on GBDT was 76.8%.The results show that the intelligent recognition method of acoustic fatigue damage based on GBDT has good recognition ability,and can effectively monitor the acoustic fatigue damage of aircraft structure panels in practical application,which verifies the effectiveness and practicability of the method.
关键词
噪声/疲劳损伤/复合材料/GBDT智能识别模型/测试验证/结构壁板Key words
noise/fatigue damage/compound material/GBDT intelligent recognition model/test verification/structural wall panels引用本文复制引用
出版年
2024
NETL
NSTL
万方数据