The Hanning self-convolution window (HSCW) is proposed in this paper. And the phase difference correction algorithm based on the discrete spectrum and the HSCW is given. The HSCW has a low peak side lobe level, a high...The Hanning self-convolution window (HSCW) is proposed in this paper. And the phase difference correction algorithm based on the discrete spectrum and the HSCW is given. The HSCW has a low peak side lobe level, a high side lobe roll-off rate, and a simple spectrum representation. Hence, leakage errors and harmonic interferences can be considerably reduced by weighting samples with the HSCW, the parameter estimation by the HSCW-based phase difference correction algorithm is free of solving high order equations, and the overall method can be easily implemented in embedded systems. Simulation and application results show that the HSCW-based phase difference correction algorithm can suppress the impacts of fundamental frequency fluctuation and white noise on harmonic parameter estimation, and the HSCW is advantageous over existing combined cosine windows in terms of harmonic analysis performance.展开更多
针对全相位频谱分析算法对采样序列中心样点有特殊要求以及当频偏量绝对值为0.5时会影响频率估计值的问题,提出了一种改进的全相位时移相位差频谱分析算法。该算法首先对序列向左循环移动一位,形成只有一位时移关系的两个序列,然后分别...针对全相位频谱分析算法对采样序列中心样点有特殊要求以及当频偏量绝对值为0.5时会影响频率估计值的问题,提出了一种改进的全相位时移相位差频谱分析算法。该算法首先对序列向左循环移动一位,形成只有一位时移关系的两个序列,然后分别进行全相位快速傅里叶变换(all phase fast Fourier transform,APFFT),计算过程中忽略相位差补偿值,避免频偏量的引入,通过两序列主谱线间相位差的直接计算便可得到信号的频率和初相估计值。仿真实验表明该算法计算简单,适用范围广,参数估计精度高且频率估计精度稳定性好。展开更多
针对奇异值分解(Singular value decomposition, SVD)的频率分离问题,研究了SVD对单个频率的分离条件,发现SVD分离单个频率的效果取决于各频率的幅值差异。若不同频率的幅值很接近,则SVD就不能分离这些频率,由此提出一种频率添加SVD算...针对奇异值分解(Singular value decomposition, SVD)的频率分离问题,研究了SVD对单个频率的分离条件,发现SVD分离单个频率的效果取决于各频率的幅值差异。若不同频率的幅值很接近,则SVD就不能分离这些频率,由此提出一种频率添加SVD算法。为了提取原信号中的特征频率,先对原信号添加该频率的理想正弦信号,使原信号中该频率成分和其他频率的幅值产生差异,从而实现对该频率成分的提取,从理论上证明此算法的可行性。仿真信号处理实例表明,即使对于频率值非常接近的两个频率,频率添加SVD算法亦可将它们准确分离,分离结果波形误差小,克服了原来SVD频率分离算法的缺陷。将此算法应用某转子系统的振动特征提取,准确地提取到振动的高阶倍频,发现高阶倍频振幅的周期性波动特征,并分析这种振幅周期性波动的原因。展开更多
基金Supported by the National Natural Science Foundation of China (Grant No.60872128)
文摘The Hanning self-convolution window (HSCW) is proposed in this paper. And the phase difference correction algorithm based on the discrete spectrum and the HSCW is given. The HSCW has a low peak side lobe level, a high side lobe roll-off rate, and a simple spectrum representation. Hence, leakage errors and harmonic interferences can be considerably reduced by weighting samples with the HSCW, the parameter estimation by the HSCW-based phase difference correction algorithm is free of solving high order equations, and the overall method can be easily implemented in embedded systems. Simulation and application results show that the HSCW-based phase difference correction algorithm can suppress the impacts of fundamental frequency fluctuation and white noise on harmonic parameter estimation, and the HSCW is advantageous over existing combined cosine windows in terms of harmonic analysis performance.
文摘针对全相位频谱分析算法对采样序列中心样点有特殊要求以及当频偏量绝对值为0.5时会影响频率估计值的问题,提出了一种改进的全相位时移相位差频谱分析算法。该算法首先对序列向左循环移动一位,形成只有一位时移关系的两个序列,然后分别进行全相位快速傅里叶变换(all phase fast Fourier transform,APFFT),计算过程中忽略相位差补偿值,避免频偏量的引入,通过两序列主谱线间相位差的直接计算便可得到信号的频率和初相估计值。仿真实验表明该算法计算简单,适用范围广,参数估计精度高且频率估计精度稳定性好。
文摘针对奇异值分解(Singular value decomposition, SVD)的频率分离问题,研究了SVD对单个频率的分离条件,发现SVD分离单个频率的效果取决于各频率的幅值差异。若不同频率的幅值很接近,则SVD就不能分离这些频率,由此提出一种频率添加SVD算法。为了提取原信号中的特征频率,先对原信号添加该频率的理想正弦信号,使原信号中该频率成分和其他频率的幅值产生差异,从而实现对该频率成分的提取,从理论上证明此算法的可行性。仿真信号处理实例表明,即使对于频率值非常接近的两个频率,频率添加SVD算法亦可将它们准确分离,分离结果波形误差小,克服了原来SVD频率分离算法的缺陷。将此算法应用某转子系统的振动特征提取,准确地提取到振动的高阶倍频,发现高阶倍频振幅的周期性波动特征,并分析这种振幅周期性波动的原因。
