共查询到18条相似文献,搜索用时 171 毫秒
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为了在噪声环境下正确实施盲源分离,采用第二代小波降噪与盲源分离相结合的方法,使噪声环境下盲源分离算法的性能得到较大地改善。并通过轴承振动信号实验研究证明该方法的优越性。 相似文献
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针对经典独立分量分析(ICA)只能应用于观测源数不少于信号源数的超定盲源分离问题,提出局部均值分解和ICA相结合的欠定盲源分离新方法。该方法将采集的单通道振动信号进行局部均值分解,基于互相关准则对分解的分量进行重组,构建虚拟噪声通道;将虚拟噪声通道与振动信号作为盲源分离的信号输入,采用基于负熵的FastICA算法实现信号源和噪声的分离,从而达到降噪目的。将该方法应用于滚动轴承故障信号,频谱分析结果表明,该方法处理后的信号中噪声得到一定程度滤除,频谱中毛刺更少,故障特征频率更加明显,有利于故障特征的提取,实验分析证明了该方法的有效性。 相似文献
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《机械工程学报》2017,(18)
将基于延时差和强度差的双耳听觉空间定位理论用于柴油机辐射噪声的分离,探索双通道噪声信号分离的可行性。另外,由于柴油机辐射噪声中不可避免的时频域混叠及同一位置辐射的噪声存在多个激励源的问题,再针对双通道定位算法分离的结果进一步利用盲源分离方法进行分离。设计柴油机振动及噪声采集试验,为屏蔽其他缸的干扰源,仅裸露6号待测缸,而对其他缸对应的机体外表面进行消音棉和铅覆盖处理。分离结果表明,双通道算法相当于一种前处理"滤波器",能排除其他位置源的干扰,针对分离出的分量,再借助盲源分离方法能准确分离出机体侧辐射噪声中的燃烧激励成分和活塞敲击激励成分。而且相比于仅仅使用盲源分离方法,该联合噪声分离方案在分离复杂的柴油机辐射噪声上更为优越。 相似文献
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基于双树复小波变换的轴承故障诊断研究 总被引:1,自引:0,他引:1
提出了一种基于双树复小波变换解调技术的轴承故障诊断新方法。该方法利用双树复小波变换具有近似平移不变性、避免频率混叠和有效降噪的优点,首先对轴承故障振动信号进行双树复小波分解和重构,将振动信号分解成实部和虚部,然后计算振动信号的双树复小波幅值包络和包络谱。齿轮箱轴承故障振动实验信号的分析表明,该方法能在强噪声环境下准确提取轴承故障产生的周期性瞬态冲击信号,能有效消除频率混叠现象和强噪声的影响,能有效识别轴承内圈和外圈故障。 相似文献
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This paper proposes a repeated blind source separation (BSS) method based on morphological filtering and singular value decomposition (SVD) to separate the mixed sources from a single-channel signal. Firstly the signal is de-noised by the morphological filter and, the noise which affects the accuracy of the separation is removed. Next, the purified signal is reconstructed in phase space, and the SVD is applied to this matrix. After choosing the effective singular values, the inverse transform is applied to the revised signal matrix. From this, the pseudo signal can be obtained. The pseudo signal and the purified original signal are used to achieve the mixed sources separation through the fast independent component analysis (FastICA) algorithm. Then, the methods above are repeated in order to separate the weaker signals. The analysis of simulation and practical application demonstrates that that proposed method shows a high level of separating performance of a single-channel signal. 相似文献
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高速列车非平稳振动信号盲源分离方法及应用 总被引:1,自引:0,他引:1
高速列车具有若干时变激励源,传统的时频分析方法只能对观测的混合振动的总体强度分布、时频域结构加以分析,不能分离出与各振源对应的信号分量从而明晰振源状态与故障特征。盲源分离是一种可行的分析方法,但由于高速列车振动信号具有时变振源数目、时变信号长度、受车速调制的变频非平稳等特征,传统的盲源分离方法不适用。为了提高高速列车非平稳信号的盲源分离效果,基于自适应滤波理论提出全局最优信噪比盲源分离新方法,并对其可分离性的判别依据进行论证。新方法的有效性经仿真计算和实测数据分析得到验证。研究表明:新方法对高速列车时变非平稳信号的盲源分离效果优于传统的基于非线性函数的盲源分离方法和基于高阶累积量的盲源分离方法。 相似文献
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研究了基于独立分量分析(independent component analysis,简称ICA)的发动机振动信号盲源分离技术,旨在将发动机振动信号按照不同的激振源进行分离。首先阐述了基于最大信噪比的盲源分离算法原理,通过对仿真信号进行分离,判断了分离输出信号与仿真信号的一致性,验证了该算法的可行性;然后将该算法与FFT分离法相结合,应用于某型双转子航空发动机高、低压转子实测振动信号盲源分离中,取得了很好的分离效果,表明应用ICA技术建立的基于最大信噪比的盲源分离算法具有迭代次数少、计算复杂度低、效果好及稳定等优点。 相似文献
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Z. M. Zhong J. Chen P. Zhong J. B. Wu 《The International Journal of Advanced Manufacturing Technology》2006,28(9-10):855-862
As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semianechoic chamber demonstrate the effectiveness of the presented methods. 相似文献
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为了实现特种管道在高温、高压、辐射等特殊环境下管壁厚度的非均匀性检测,提出一种基于微分算法的管道壁厚激光超声测量及特征信号处理方法。采用脉冲激光激励和激光干涉探测的激光超声方法,实验测得管道试件的宽频带激光超声信号。采用数字平均算法对宽带激光超声信号进行去噪处理,提高原始激光超声信号的信噪比。采用微分算法对激光超声信号进行特征提取处理,得到表征管壁厚度的激光超声特征信号。根据管道材料声速和激光超声传播时间反演计算得到管道试件的壁厚值,管壁厚度测量值与实际值的误差小于5%。研究表明,基于微分算法的管道壁厚激光超声测量及特征信号处理方法具有良好的信噪比、准确的信号特征量和较高的测量精度,可用于管道壁厚的在线实时检测以及因腐蚀、应力引起的管道壁厚不均匀性检测。 相似文献
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Z.M. Zhong J. Chen P. Zhong J.B. Wu 《The International Journal of Advanced Manufacturing Technology》2006,28(9):855-862
As the result of vibration emission in air, a machine sound signal carries important information about the working condition
of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features
of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition
must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored
source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed
that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound
sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white
noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS
analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation
algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer
channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via
this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained
from the observed signals recorded in a semi-anechoic chamber demonstrate the effectiveness of the presented methods . 相似文献