首页> 外文期刊>International journal of structural stability and dynamics >Detection of Structural Damage in Rotating Beams Using Modal Sensitivity Analysis and Sparse Regularization
【24h】

Detection of Structural Damage in Rotating Beams Using Modal Sensitivity Analysis and Sparse Regularization

机译:使用模态敏感性分析和稀疏正则化旋转梁结构损坏的检测

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Rotating beams are often encountered in the wind turbines and the rotors, and detection of the damages in rotating beams as earlier as possible is central to ensuring the safety and serviceability of practical structures. To this end, a modal sensitivity approach in conjunction with the sparse regularization is proposed in this paper. First, the eigen equations for the flap-wise and chord-wise vibrations of a rotating beam are established upon Hamilton's principle. Then, damage detection is formulated as a nonlinear least-squares problem that finds the damage coefficients to minimize the error between the measured and calculated data. To solve the nonlinear least-squares problem, the sensitivity method that requires the modal sensitivity analysis is developed. In real applications, damage detection is usually an ill-posed problem and to circumvent the ill-posedness, the sparse regularization is introduced due to the fact that the numbers of actual damage locations are often scarce. Numerical examples are studied and results show that the proposed approach is more accurate than the enhanced sensitivity approach and the flap-wise modal data outperforms the chord-wise modal data in damage detection of rotating beams.
机译:在风力涡轮机和转子中经常遇到旋转梁,并且在尽可能较早的旋转梁中的损坏是可确保实际结构的安全性和可用性的旋转梁的损坏。为此,本文提出了一种与稀疏正则化的模态灵敏度方法。首先,在汉密尔顿的原理上建立了旋转光束的襟翼和和弦振动的特征方程。然后,将损坏检测标配制成非线性最小二乘问题,该问题发现损坏系数,以最小化测量和计算的数据之间的误差。为了解决非线性最小二乘问题,开发了需要模态灵敏度分析的灵敏度方法。在真实的应用中,损伤检测通常是一个不良问题,并且规避不良姿势,由于实际损坏位置的数量通常稀缺而引入稀疏的正则化。研究了数值例子,结果表明,所提出的方法比增强的灵敏度方法更准确,襟翼方面的模态数据越高,旋转梁的损伤检测中的和弦模式模型。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

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