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损伤信号指标
相关语句
  damage signature
    In order to detect the damage of symmetric frame structures effectively, the input parameter of BP Neural Networks has been discussed, and a composite input vectors include the Damage Signature has been constructed.
    为了有效地检测对称框架结构的损伤,讨论了BP网络的输入参数,并构造了包含损伤信号指标的组合损伤向量。
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  “损伤信号指标”译为未确定词的双语例句
    Theoretical analysis concludes that the influence of uniform modeling errors may be minimized or even eliminated when{NFCR i}or{NDSI 1}is chosen to be the input vector to ANN.
    理论分析表明 ,用归一后的频率变化比或按模态点归一的一阶模态损伤信号指标构造神经网络的输入向量 ,能有效地降低甚至消除均匀建模误差的影响。
短句来源
    Simulation results show that the first 10 natural frequencies change ratios and the damage signal indexes corresponding to the first mode are sensitive to the damage location of load-bearing cables,and the first natural frequency square change ratio is effective for identification of damage extent of load-bearing cables.
    研究表明,虽然空间索杆结构的动力性能较为复杂,但归一化后的结构前10阶固有频率变化比和归一化后的对应结构第一阶模态的部分节点的损伤信号指标对承重索的损伤位置较为敏感,利用其进行损伤定位是可行的。 在此基础上,再增加考虑结构的第一阶固有频率平方变化比即可进一步有效识别出承重索的损伤程度。
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  damage signature
A Java program is written to deduct the unwanted features from the damage signature.
      
This has enabled us to investigate the damage signature and damage tolerance of both panels.
      
The damage signature vectors from the damage signature database cannot be used directly as it contains unnecessary features which need to be removed.
      
The filtered data is then stored in a database called the processed damage signature database.
      
The situation is far more complex for the damage signature vectors that have been obtained from sensors placed at an offset from the bondline.
      


The influence of modeling errors on input vectors to artificial neural networks(ANN) is studied.Relative error formulas of five input vectors,applicable to the case of uniform modeling errors,are derived,which are instructive for selecting the input vectors to ANN.Theoretical analysis concludes that the influence of uniform modeling errors may be minimized or even eliminated when{NFCR i}or{NDSI 1}is chosen to be the input vector to ANN.All the formulas and conclusions are verified by numerical results for...

The influence of modeling errors on input vectors to artificial neural networks(ANN) is studied.Relative error formulas of five input vectors,applicable to the case of uniform modeling errors,are derived,which are instructive for selecting the input vectors to ANN.Theoretical analysis concludes that the influence of uniform modeling errors may be minimized or even eliminated when{NFCR i}or{NDSI 1}is chosen to be the input vector to ANN.All the formulas and conclusions are verified by numerical results for damage detection on a beam and on a frame.Furthermore,the numerical simulation analysis for damage identification of the same frame while with nonuniform modeling errors is also made for comparison,and some qualitative viewpoints are presented.

通过理论推导 ,得到了模型参数误差均匀分布时五种输入向量相对误差的计算公式 ,这些公式为结构损伤识别神经网络的输入向量的选择提供了理论指导。理论分析表明 ,用归一后的频率变化比或按模态点归一的一阶模态损伤信号指标构造神经网络的输入向量 ,能有效地降低甚至消除均匀建模误差的影响。这些公式和结论在梁和框架损伤识别的数值算例中得到了证实。此外 ,本文对非均匀建模误差对神经网络输入向量及识别结果的影响进行了数值模拟和分析。

Using the back-propagation neural network and the damage identification method based on structural dynamic responses,prestress-looses of single load-bearing cable and double load-bearing cables in a practical spatial cable-strut structure are diagnosed.Simulation results show that the first 10 natural frequencies change ratios and the damage signal indexes corresponding to the first mode are sensitive to the damage location of load-bearing cables,and the first natural frequency square change ratio is effective...

Using the back-propagation neural network and the damage identification method based on structural dynamic responses,prestress-looses of single load-bearing cable and double load-bearing cables in a practical spatial cable-strut structure are diagnosed.Simulation results show that the first 10 natural frequencies change ratios and the damage signal indexes corresponding to the first mode are sensitive to the damage location of load-bearing cables,and the first natural frequency square change ratio is effective for identification of damage extent of load-bearing cables.

针对某实际空间索杆结构的承重索预应力松弛现象,采用BP神经网络与基于振动的损伤识别方法,分别对单榀承重索和双榀承重索的预应力松弛进行了识别研究。研究表明,虽然空间索杆结构的动力性能较为复杂,但归一化后的结构前10阶固有频率变化比和归一化后的对应结构第一阶模态的部分节点的损伤信号指标对承重索的损伤位置较为敏感,利用其进行损伤定位是可行的。在此基础上,再增加考虑结构的第一阶固有频率平方变化比即可进一步有效识别出承重索的损伤程度。

 
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