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颤振边界预测
相关语句
  flutter boundary prediction
    This paper treats ACSE algorithm as a time-frequency domain technique and applies it to processing of flutter signals obtained in an aeroelastic model test in a low-speed wind tunnel. The results show preliminarily that the precision of flutter boundary prediction can be improved by 1%~3%.
    同时 ,该算法作为一种时频域滤波技术 ,成功应用于某型飞机的颤振试验信号处理中 ,使得颤振边界预测的精度提高了 1 %~ 3%
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    Focused on the nonstationary features and analysis requirements of flutter test data, the application scheme is brought forward for flutter signal processing.
    本文基于颤振试验信号的非平稳特点与分析需求,应用自适应时频分析技术给出并实现了两种新的颤振信号处理方案,重点研究了适用于连续变速颤振试验的颤振边界预测问题。
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Refs.3 and 4 dealt with chirplet decomposition. In using Refs.3 and 4, we found that estimating initial values is one key problem that must be carefully dealt with. So, in this paper on ACSE algorithm, we lay particular emphasis on discussing how to estimate initial values. ACSE algorithm is a new algorithm based on chirplet elementary functions. This new algorithm obtains initial value estimation and precise resolution simultaneously and expands signal adaptively as the sum of chirplet elementary functions....

Refs.3 and 4 dealt with chirplet decomposition. In using Refs.3 and 4, we found that estimating initial values is one key problem that must be carefully dealt with. So, in this paper on ACSE algorithm, we lay particular emphasis on discussing how to estimate initial values. ACSE algorithm is a new algorithm based on chirplet elementary functions. This new algorithm obtains initial value estimation and precise resolution simultaneously and expands signal adaptively as the sum of chirplet elementary functions. According to expansion coefficients and elementary function parameters, adaptive time-frequency energy distribution is obtained. The signal is reconstructed well and noise is also effectively reduced. We tabulated simulation results for ten parameters (five design parameters and five estimated parameters), giving amplitude, standard deviation, time, frequency, and frequency modulation rate for each of these ten parameters. Comparison of simulation results for each signal parameter with those for its corresponding estimated parameter shows that estimation precision of ACSE algorithm is very high. This paper treats ACSE algorithm as a time-frequency domain technique and applies it to processing of flutter signals obtained in an aeroelastic model test in a low-speed wind tunnel. The results show preliminarily that the precision of flutter boundary prediction can be improved by 1%~3%.

提出了一种新的以高斯线调频小波作为基函数的自适应信号展开算法。算法融参数的初值估计和精确估计于一体 ,自适应地将信号展开在高斯线调频小波基函数集上 ,通过展开系数和基函数参数获得信号的自适应时频能量分布。数值仿真结果表明该算法抗噪性好 ,信号可重构性高。同时 ,该算法作为一种时频域滤波技术 ,成功应用于某型飞机的颤振试验信号处理中 ,使得颤振边界预测的精度提高了 1 %~ 3%

The accuracy of modal parameter estimation plays a crucial role in flutter boundary prediction. A new wavelet denoising method is introduced for flight flutter testing data, which can improve the estimation of frequency domain identification algorithms. In this method, the testing data is first preprocessed with a gradient inverse weighted filter to initially lower the noise. The redundant wavelet transform is then used to decompose the signal into several levels. A “clean” input is recovered from the noisy...

The accuracy of modal parameter estimation plays a crucial role in flutter boundary prediction. A new wavelet denoising method is introduced for flight flutter testing data, which can improve the estimation of frequency domain identification algorithms. In this method, the testing data is first preprocessed with a gradient inverse weighted filter to initially lower the noise. The redundant wavelet transform is then used to decompose the signal into several levels. A “clean” input is recovered from the noisy data by level dependent thresholding approach, and the noise of output is reduced by a modified spatially selective noise filtration technique. The advantage of the wavelet denoising is illustrated by means of simulated and real data.

准确估计模态参数在飞机颤振边界的预测中具有重要意义。为了提高频域辨识算法的辨识效果 ,提出了一种用于颤振飞行试验数据处理的小波去噪方法。该方法引入梯度倒数加权滤波器对数据进行预处理 ,处理后的数据运用冗余小波进行小波分解 ,然后对输入信号在不同尺度下分别进行阈值降噪 ,对输出信号则采用了一种改进的小波空域相关滤波法去噪。最后通过仿真计算和实际数据证明该方法有效

 
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