Vibration acceleration signals are often measured from case surface of arunning machine to monitor its condition. If the measured vibration signals display to have periodicimpulse components with a certain frequency, ...Vibration acceleration signals are often measured from case surface of arunning machine to monitor its condition. If the measured vibration signals display to have periodicimpulse components with a certain frequency, there may exist a corresponding local fault in themachine, and if further extracting the periodic impulse components from the vibration signals, theseverity of the local fault can be estimated and tracked. However, the signal-to-noise ratios (SNRs)of the vibration acceleration signals are often so small that the periodic impulse components aresubmersed in much background noises and other components, and it is difficult or inconvenient for usto detect and extract the periodic impulse components with the current common analyzing methods forvibration signals. Therefore, another technique, called singular value decomposition (SVD), istried to be introduced to solve the problem. First, the principle of detecting and extracting thesignal periodic components using singular value decomposition is summarized and discussed. Second,the infeasibility of the direct use of the existing SVD based detecting and extracting approach ispointed out. Third, the approach to construct the matrix for SVD from the signal series is improvedlargely, which is the key program to improve the SVD technique; Other associated improvement is alsoproposed. Finally, a simulating application example and a real-life application example ondetecting and extracting the periodic impulse components are given, which showed that the introducedand improved SVD technique is feasible.展开更多
We study the problem of parameter estimation for mean-reverting α-stable motion, dXt = （a0 - θ0Xt）dt ＋ dZt, observed at discrete time instants. A least squares estimator is obtained and its asymptotics is discuss...We study the problem of parameter estimation for mean-reverting α-stable motion, dXt = （a0 - θ0Xt）dt ＋ dZt, observed at discrete time instants. A least squares estimator is obtained and its asymptotics is discussed in the singular case （a0, θ0） = （0, 0）. If a0 = 0, then the mean-reverting α-stable motion becomes Ornstein-Uhlenbeck process and is studied in [7] in the ergodic case θ0 〉 0. For the Ornstein-Uhlenbeck process, asymptotics of the least squares estimators for the singular case （θ0 = 0） and for ergodic case （θ0 〉 0） are completely different.展开更多
In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-H...In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.展开更多
针对工况传递路径分析(operational transfer path analysis,OTPA)测得振动信号存在大量高频噪声的问题,提出一种基于变分模态分解(variational mode decomposition,VMD)和奇异值分解(singular value decomposition,SVD)的组合降噪方法V...针对工况传递路径分析(operational transfer path analysis,OTPA)测得振动信号存在大量高频噪声的问题,提出一种基于变分模态分解(variational mode decomposition,VMD)和奇异值分解(singular value decomposition,SVD)的组合降噪方法VMD-SVD。该方法通过VMD算法对原始含噪信号进行分解,得到K个本征模态分量(intrinsic mode function,IMF);再通过方差贡献率(VCR)滤除含噪信号较大的IMF分量,并保留有效成分较多的IMF分量,经SVD算法对保留的IMF分量进行降噪处理;最后将降噪处理后的信号进行重构,得到本文组合降噪处理后的信号。本文通过模拟仿真实验验证上述方法的降噪效果,并将该方法运用到OTPA采集振动信号中。与其他基本降噪方法进行对比的结果表明,该方法能够有效滤除采集振动信号中的高频噪声,提高了OTPA方法的准确度以及信号后续分析处理的可靠性。展开更多
为了解决张量鲁棒主成分分析(tensor robust principal component analysis,TRPCA)还原低秩结构时同等收缩奇异值造成的信息提取偏差问题,本文考虑区别对待奇异值,使用非凸加权张量Schatten-p范数(0<p<1)分析张量数据,可减少对奇...为了解决张量鲁棒主成分分析(tensor robust principal component analysis,TRPCA)还原低秩结构时同等收缩奇异值造成的信息提取偏差问题,本文考虑区别对待奇异值,使用非凸加权张量Schatten-p范数(0<p<1)分析张量数据,可减少对奇异值的惩罚。为解决数据受损严重难以恢复的问题,提出低秩预分离的方法实现近似低秩部分和近似稀疏部分的预先分离;为增强高阶张量之间相关性同时降低数据对特定噪声的敏感性,提出随机抖动正则器的机制对预分离后成分分别选取随机区域优化,利用噪声信息的随机性来正则化算法得以约束模型的复杂度;最后使用不同类型的图像数据集,包括彩色图像、核磁共振图像、高光谱及多光谱图像和灰度视频,进行高维数据恢复实验。结果表明该方法在图像恢复性能上明显优于其他TRPCA方法,并且在数据受损严重时同样具有优势,有效提取主成分信息的同时减小数据对特定噪声的依赖,具有较强的鲁棒性和适应性,可为TRPCA方法在图像恢复领域中提供参考。展开更多
作为解决信息过载问题的有效方式,推荐系统能够根据用户偏好对海量信息进行过滤,为用户提供个性化的推荐.但在推荐过程中,性能表现优异的协同过滤模型并没有充分利用上下文信息,这在一定程度上使系统面临性能瓶颈.为了进一步提高系统性...作为解决信息过载问题的有效方式,推荐系统能够根据用户偏好对海量信息进行过滤,为用户提供个性化的推荐.但在推荐过程中,性能表现优异的协同过滤模型并没有充分利用上下文信息,这在一定程度上使系统面临性能瓶颈.为了进一步提高系统性能,从评分上下文信息着手,通过对项目评分进行分类统计获得评分奇异性,同时借鉴多渠道扩散相似性模型将推荐系统作为用户-项目二分网络的思想,提出了融合奇异性和扩散过程的协同过滤模型(collaborative filtering model fusing singularity and diffusion process,简称CFSDP).为了表明模型的优越性,比较实验基于MovieLens,NetFlix和Jester这3个不同的数据集展开.实验结果表明,该模型不仅具有良好的扩展性,而且在合理的时间开销下,可以显著提高系统的预测和推荐质量.展开更多
提出多分辨奇异值分解(Multi-resolution singular value decomposition,MRSVD)的概念,基于矩阵二分递推构造原理,利用奇异值分解(Singular value decomposition,SVD)获得具有不同分辨率的近似和细节信号,以多分辨率来展现信号不同层次...提出多分辨奇异值分解(Multi-resolution singular value decomposition,MRSVD)的概念,基于矩阵二分递推构造原理,利用奇异值分解(Singular value decomposition,SVD)获得具有不同分辨率的近似和细节信号,以多分辨率来展现信号不同层次的概貌和细部特征。给出MRSVD的分解和重构算法,并从理论上证明这种分解方式的多分辨分析特性。研究结果表明,MRSVD可以精确地检测出信号中的奇异点位置,克服小波检测时的奇异点偏移缺陷,并具有优良的消噪能力,可实现零相移消噪,此外还具有微弱故障特征提取能力,在对一个轴承振动信号的处理中,提取到其中隐藏的周期性冲击特征,实现对轴承损伤的准确诊断。相应地与小波变换结果进行比较,证明MRSVD在信号处理和故障诊断领域是一种很有应用前景的方法。展开更多
基金This project is supported by National Natural Science Foundation of China (No.59905011, 60275041).
文摘Vibration acceleration signals are often measured from case surface of arunning machine to monitor its condition. If the measured vibration signals display to have periodicimpulse components with a certain frequency, there may exist a corresponding local fault in themachine, and if further extracting the periodic impulse components from the vibration signals, theseverity of the local fault can be estimated and tracked. However, the signal-to-noise ratios (SNRs)of the vibration acceleration signals are often so small that the periodic impulse components aresubmersed in much background noises and other components, and it is difficult or inconvenient for usto detect and extract the periodic impulse components with the current common analyzing methods forvibration signals. Therefore, another technique, called singular value decomposition (SVD), istried to be introduced to solve the problem. First, the principle of detecting and extracting thesignal periodic components using singular value decomposition is summarized and discussed. Second,the infeasibility of the direct use of the existing SVD based detecting and extracting approach ispointed out. Third, the approach to construct the matrix for SVD from the signal series is improvedlargely, which is the key program to improve the SVD technique; Other associated improvement is alsoproposed. Finally, a simulating application example and a real-life application example ondetecting and extracting the periodic impulse components are given, which showed that the introducedand improved SVD technique is feasible.
基金Hu is supported by the National Science Foundation under Grant No.DMS0504783Long is supported by FAU Start-up funding at the C. E. Schmidt College of Science
文摘We study the problem of parameter estimation for mean-reverting α-stable motion, dXt = （a0 - θ0Xt）dt ＋ dZt, observed at discrete time instants. A least squares estimator is obtained and its asymptotics is discussed in the singular case （a0, θ0） = （0, 0）. If a0 = 0, then the mean-reverting α-stable motion becomes Ornstein-Uhlenbeck process and is studied in [7] in the ergodic case θ0 〉 0. For the Ornstein-Uhlenbeck process, asymptotics of the least squares estimators for the singular case （θ0 = 0） and for ergodic case （θ0 〉 0） are completely different.
文摘In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.
文摘针对工况传递路径分析(operational transfer path analysis,OTPA)测得振动信号存在大量高频噪声的问题,提出一种基于变分模态分解(variational mode decomposition,VMD)和奇异值分解(singular value decomposition,SVD)的组合降噪方法VMD-SVD。该方法通过VMD算法对原始含噪信号进行分解,得到K个本征模态分量(intrinsic mode function,IMF);再通过方差贡献率(VCR)滤除含噪信号较大的IMF分量,并保留有效成分较多的IMF分量,经SVD算法对保留的IMF分量进行降噪处理;最后将降噪处理后的信号进行重构,得到本文组合降噪处理后的信号。本文通过模拟仿真实验验证上述方法的降噪效果,并将该方法运用到OTPA采集振动信号中。与其他基本降噪方法进行对比的结果表明,该方法能够有效滤除采集振动信号中的高频噪声,提高了OTPA方法的准确度以及信号后续分析处理的可靠性。
文摘为了解决张量鲁棒主成分分析(tensor robust principal component analysis,TRPCA)还原低秩结构时同等收缩奇异值造成的信息提取偏差问题,本文考虑区别对待奇异值,使用非凸加权张量Schatten-p范数(0<p<1)分析张量数据,可减少对奇异值的惩罚。为解决数据受损严重难以恢复的问题,提出低秩预分离的方法实现近似低秩部分和近似稀疏部分的预先分离;为增强高阶张量之间相关性同时降低数据对特定噪声的敏感性,提出随机抖动正则器的机制对预分离后成分分别选取随机区域优化,利用噪声信息的随机性来正则化算法得以约束模型的复杂度;最后使用不同类型的图像数据集,包括彩色图像、核磁共振图像、高光谱及多光谱图像和灰度视频,进行高维数据恢复实验。结果表明该方法在图像恢复性能上明显优于其他TRPCA方法,并且在数据受损严重时同样具有优势,有效提取主成分信息的同时减小数据对特定噪声的依赖,具有较强的鲁棒性和适应性,可为TRPCA方法在图像恢复领域中提供参考。
文摘作为解决信息过载问题的有效方式,推荐系统能够根据用户偏好对海量信息进行过滤,为用户提供个性化的推荐.但在推荐过程中,性能表现优异的协同过滤模型并没有充分利用上下文信息,这在一定程度上使系统面临性能瓶颈.为了进一步提高系统性能,从评分上下文信息着手,通过对项目评分进行分类统计获得评分奇异性,同时借鉴多渠道扩散相似性模型将推荐系统作为用户-项目二分网络的思想,提出了融合奇异性和扩散过程的协同过滤模型(collaborative filtering model fusing singularity and diffusion process,简称CFSDP).为了表明模型的优越性,比较实验基于MovieLens,NetFlix和Jester这3个不同的数据集展开.实验结果表明,该模型不仅具有良好的扩展性,而且在合理的时间开销下,可以显著提高系统的预测和推荐质量.
文摘提出多分辨奇异值分解(Multi-resolution singular value decomposition,MRSVD)的概念,基于矩阵二分递推构造原理,利用奇异值分解(Singular value decomposition,SVD)获得具有不同分辨率的近似和细节信号,以多分辨率来展现信号不同层次的概貌和细部特征。给出MRSVD的分解和重构算法,并从理论上证明这种分解方式的多分辨分析特性。研究结果表明,MRSVD可以精确地检测出信号中的奇异点位置,克服小波检测时的奇异点偏移缺陷,并具有优良的消噪能力,可实现零相移消噪,此外还具有微弱故障特征提取能力,在对一个轴承振动信号的处理中,提取到其中隐藏的周期性冲击特征,实现对轴承损伤的准确诊断。相应地与小波变换结果进行比较,证明MRSVD在信号处理和故障诊断领域是一种很有应用前景的方法。
