针对目前轴承行业故障分析诊断中普遍存在故障数据来源较少、涉及所属工况较多的难点问题,提出了基于MFEC(Mean Filling and Energy Conservation)的数据增强算法。首先对原始振动信号进行时域重采样;并使用重采样后信号的均值填充来保...针对目前轴承行业故障分析诊断中普遍存在故障数据来源较少、涉及所属工况较多的难点问题,提出了基于MFEC(Mean Filling and Energy Conservation)的数据增强算法。首先对原始振动信号进行时域重采样;并使用重采样后信号的均值填充来保证信号的采样长度不变;由于信号的能量会发生改变,防止对结果产生影响,需要设置参数调整幅值使之能量守恒。之后对扩充的样本数据进行分数阶傅里叶变换(FRFT),得到不同阶次的一维信号。最后将这些一维信号作为深度学习网络(VGG-16)的输入,在神经网络中学习训练,实现轴承的故障诊断。实验表明,该方法有效地增加样本的数量及其多样性,并且不同阶次的处理信号对不同故障类型的诊断具有良好的针对性,有效提高故障诊断的分类效率和准确率。展开更多
针对高速移动环境下的正交时频空间(orthogonal time frequency space,OTFS)调制系统,提出了一种基于双啁啾信号分数阶傅里叶变换(fractional Fourier transform,FrFT)的定时和频偏联合同步算法,充分利用双啁啾信号的抗多普勒频移性能和...针对高速移动环境下的正交时频空间(orthogonal time frequency space,OTFS)调制系统,提出了一种基于双啁啾信号分数阶傅里叶变换(fractional Fourier transform,FrFT)的定时和频偏联合同步算法,充分利用双啁啾信号的抗多普勒频移性能和FrFT的能量聚集特性,实现高多普勒频移条件下的精确同步.采用时延-多普勒(delay-Doppler,DD)域双啁啾训练序列,在接收端对接收信号执行FrFT,通过捕获双峰并计算峰值位置偏移实现定时同步和频率偏移估计.仿真结果表明,在整个信噪比范围内表现出整体最优的定时同步性能,且在频偏估计方面具有优势.此外,该算法在具有高功率导频干扰的OTFS系统中也呈现出优越的同步性能.展开更多
Accurate detection of exercise fatigue based on physiological signals is vital for reason-able physical activity.As a non-invasive technology,phonocardiogram(PCG)signals possess arobust capability to reflect cardiovas...Accurate detection of exercise fatigue based on physiological signals is vital for reason-able physical activity.As a non-invasive technology,phonocardiogram(PCG)signals possess arobust capability to reflect cardiovascular information,and their data acquisition devices are quiteconvenient.In this study,a novel hybrid approach of fractional Fourier transform(FRFT)com-bined with linear and discrete wavelet transform(DWT)features extracted from PCG is proposedfor PCG multi-class classification.The proposed system enhances the fatigue detection performanceby combining optimized FRFT features with an effective aggregation of linear features and DWTfeatures.The FRFT technique is employed to convert the 1-D PCG signal into 2-D image which issent to a pre-trained convolutional neural network structure,called VGG-16.The features from theVGG-16 were concatenated with the linear and DWT features to form fused features.The fusedfeatures are sent to support vector machine(SVM)to distinguish six distinct fatigue levels.Experi-mental results demonstrate that the proposed fused features outperform other feature combinationssignificantly.展开更多
针对Chirp基调制信号在分数阶傅里叶变换域特征明显,信号周期易被检测等问题,提出一种能够实现多域隐蔽的低检测概率(low probability of detection,LPD)波形构造方法。该方法采用分数阶傅里叶变换跳频(fractional Fourier transform-fr...针对Chirp基调制信号在分数阶傅里叶变换域特征明显,信号周期易被检测等问题,提出一种能够实现多域隐蔽的低检测概率(low probability of detection,LPD)波形构造方法。该方法采用分数阶傅里叶变换跳频(fractional Fourier transform-frequency hopping,FrFT-FH)架构,在不改变Chirp信号扩频增益的前提下,通过时宽分割和重组(time width division and reorganization,TDR),降低信号在分数阶傅里叶变换域和周期域的能量聚敛特性。仿真结果表明,相较于现有LPD波形只能实现单一特征域隐蔽的问题,所提波形在不影响系统通信性能的前提下,面对频域检测、分数阶傅里叶变换域检测、周期域检测多种检测手段,在10 dB信噪比条件下的信号检测概率均低于0.2,满足系统在不同特征域下的LPD需求。展开更多
基于分数阶傅里叶变换(Fractional Fourier Transform,FRFT)对线性调频(Linear Frequency Modulated,LFM)信号参数进行估计,问题关键是确定FRFT最佳阶数,根据误差迭代思想提出新的参数估计算法,该算法利用归一化带宽和旋转角的转化关系...基于分数阶傅里叶变换(Fractional Fourier Transform,FRFT)对线性调频(Linear Frequency Modulated,LFM)信号参数进行估计,问题关键是确定FRFT最佳阶数,根据误差迭代思想提出新的参数估计算法,该算法利用归一化带宽和旋转角的转化关系,由估计误差推算角度差值,有效降低了运算量,不需要调频斜率正负的先验信息,改进的对数搜索算法可以进一步提高参数估计结果的稳定性和可靠性。仿真结果表明,信噪比在-8 dB以上时该方法在高效率的前提下仍具有良好的参数估计性能,平均估计误差在1%以内,估计结果接近Cramer-Rao下限,满足工程实时处理需求。展开更多
文摘针对目前轴承行业故障分析诊断中普遍存在故障数据来源较少、涉及所属工况较多的难点问题,提出了基于MFEC(Mean Filling and Energy Conservation)的数据增强算法。首先对原始振动信号进行时域重采样;并使用重采样后信号的均值填充来保证信号的采样长度不变;由于信号的能量会发生改变,防止对结果产生影响,需要设置参数调整幅值使之能量守恒。之后对扩充的样本数据进行分数阶傅里叶变换(FRFT),得到不同阶次的一维信号。最后将这些一维信号作为深度学习网络(VGG-16)的输入,在神经网络中学习训练,实现轴承的故障诊断。实验表明,该方法有效地增加样本的数量及其多样性,并且不同阶次的处理信号对不同故障类型的诊断具有良好的针对性,有效提高故障诊断的分类效率和准确率。
文摘针对高速移动环境下的正交时频空间(orthogonal time frequency space,OTFS)调制系统,提出了一种基于双啁啾信号分数阶傅里叶变换(fractional Fourier transform,FrFT)的定时和频偏联合同步算法,充分利用双啁啾信号的抗多普勒频移性能和FrFT的能量聚集特性,实现高多普勒频移条件下的精确同步.采用时延-多普勒(delay-Doppler,DD)域双啁啾训练序列,在接收端对接收信号执行FrFT,通过捕获双峰并计算峰值位置偏移实现定时同步和频率偏移估计.仿真结果表明,在整个信噪比范围内表现出整体最优的定时同步性能,且在频偏估计方面具有优势.此外,该算法在具有高功率导频干扰的OTFS系统中也呈现出优越的同步性能.
基金the National Natural Sci-ence Foundation of China(No.62301056)the Fundamental Research Funds for Central Universities(No.2022QN005).
文摘Accurate detection of exercise fatigue based on physiological signals is vital for reason-able physical activity.As a non-invasive technology,phonocardiogram(PCG)signals possess arobust capability to reflect cardiovascular information,and their data acquisition devices are quiteconvenient.In this study,a novel hybrid approach of fractional Fourier transform(FRFT)com-bined with linear and discrete wavelet transform(DWT)features extracted from PCG is proposedfor PCG multi-class classification.The proposed system enhances the fatigue detection performanceby combining optimized FRFT features with an effective aggregation of linear features and DWTfeatures.The FRFT technique is employed to convert the 1-D PCG signal into 2-D image which issent to a pre-trained convolutional neural network structure,called VGG-16.The features from theVGG-16 were concatenated with the linear and DWT features to form fused features.The fusedfeatures are sent to support vector machine(SVM)to distinguish six distinct fatigue levels.Experi-mental results demonstrate that the proposed fused features outperform other feature combinationssignificantly.
文摘针对Chirp基调制信号在分数阶傅里叶变换域特征明显,信号周期易被检测等问题,提出一种能够实现多域隐蔽的低检测概率(low probability of detection,LPD)波形构造方法。该方法采用分数阶傅里叶变换跳频(fractional Fourier transform-frequency hopping,FrFT-FH)架构,在不改变Chirp信号扩频增益的前提下,通过时宽分割和重组(time width division and reorganization,TDR),降低信号在分数阶傅里叶变换域和周期域的能量聚敛特性。仿真结果表明,相较于现有LPD波形只能实现单一特征域隐蔽的问题,所提波形在不影响系统通信性能的前提下,面对频域检测、分数阶傅里叶变换域检测、周期域检测多种检测手段,在10 dB信噪比条件下的信号检测概率均低于0.2,满足系统在不同特征域下的LPD需求。
