• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks
【2h】

Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks

机译：基于完全连接的神经网络和卷积神经网络的复合转子的结构损伤识别

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Damage identification of composite structures is a major ongoing challenge for a secure operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a cost-intensive maintenance service has to be performed in order to avoid critical failure. A major advantage of composite structures is that they are able to safely operate after damage initiation and under ongoing damage propagation. Therefore, a robust, efficient diagnostic damage identification method would allow monitoring the damage process with intervention occurring only when necessary. This study investigates the structural vibration response of composite rotors by applying machine learning methods and the ability to identify, localise and quantify the present damage. To this end, multiple fully connected neural networks and convolutional neural networks were trained on vibration response spectra from damaged composite rotors with barely visible damage, mostly matrix cracks and local delaminations using dimensionality reduction and data augmentation. A databank containing 720 simulated test cases with different damage states is used as a basis for the generation of multiple data sets. The trained models are tested using k-fold cross validation and they are evaluated based on the sensitivity, specificity and accuracy. Convolutional neural networks perform slightly better providing a performance accuracy of up to 99.3% for the damage localisation and quantification.
机译：由于复合材料的复杂，逐渐损伤行为，复合结构的损伤识别是一种主要的持续挑战，对于安全的复合材料的复杂逐渐损伤行为。特别是对于空气发动机和风力涡轮机中的复合转子，必须进行成本密集的维护服务，以避免临界失败。复合结构的主要优点是它们能够在损坏开始并在持续的损伤传播之后安全地操作。因此，稳健，有效的诊断损坏识别方法将允许在必要时监视止干预的损坏过程。本研究通过施加机器学习方法和识别，定位和量化当前损伤的能力来研究复合转子的结构振动响应。为此，多个完全连接的神经网络和卷积神经网络训练从损坏的复合转子具有几乎具有几乎可见的损坏的振动响应光谱，主要使用维数减少和数据增强的矩阵裂缝和局部分层。包含具有不同损坏状态的720个模拟测试用例的数据库被用作生成多个数据集的基础。使用k折叠交叉验证测试培训的模型，并根据灵敏度，特异性和准确性进行评估。卷积神经网络略微更好地提供损坏定位和量化的性能准确性高达99.3％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号