共查询到17条相似文献,搜索用时 171 毫秒
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基于时频分析的欠定信号盲分离与微弱特征提取 总被引:2,自引:0,他引:2
盲源分离对于多振源信号的故障诊断与识别是一种有效的方法,但是传统的盲源分离算法都是针对观察信号大于或等于源信号的情况,但对于观察信号小于源信号的欠定盲分离问题,这在很大程度上制约了盲源分离的实际应用。通过应用经验模式分解和时频分析对非平稳信号分析的优势,提出基于时频分析的欠定盲源分离方法进行设备微弱特征提取。对振动信号进行经验模式分解,并根据分解得到的内蕴模式分量估计源信号个数并选择最优的观察信号,将振动信号与选择的最优观察信号组成新的观察信号进行基于时频分析的盲源分离,通过对仿真信号和齿轮箱实测信号进行验证分析。并与基于独立分量分析的盲源分离算法进行对比,研究表明基于时频分析的盲源分离对混合信号具有更好的分离效果,能够较好地对微弱特征进行提取。 相似文献
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传统盲源分离方法要求传感器观测信号数目不小于源信号数目,且在源信号平稳、相互独立的前提下,才能得到较为准确的分离信号,但对于发动机缸盖振动非平稳信号,由于激励源较多,这些条件不易满足。为实现缸盖振动信号盲源分离,提出了基于阶比滤波的单通道缸盖振动信号盲源分离方法。利用燃爆激励信号频率随转频变化的先验信息,通过阶比滤波得到阶比分量,将阶比分量和单通道信号组成多维观测信号,通过快速独立成分分析方法得到了缸盖振动非平稳信号的分离信号。仿真和应用研究证明了该方法的有效性。 相似文献
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航空发动机转子振动信号的分离测试技术 总被引:1,自引:3,他引:1
在传统谱分析方法的基础上,尝试应用盲源分离技术对飞机发动机振动信号进行振源分离.首先,介绍了发动机振动信号的基本处理方法和常见的发动机故障类型及特征,引入了盲源分离理论并讨论了其在航空发动机振动信号处理中应用的可行性.然后,对某型涡扇发动机振动过大的现象进行了故障诊断分析.最后,应用FastICA和JADE算法对检测的振动信号进行分析,分离出了发动机的振源信号.这说明发动机振动信号分析采用盲源分离与谱分析相结合的技术可以有效分离振源信号,提高故障诊断的准确性. 相似文献
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研究了基于最大信噪比的盲源分离算法,并将其应用在汽车变速箱振动信号分析上,其目的是在变速箱工作时将不同激振源分离出来以便进行故障诊断。通过计算机进行信号分离实验仿真,验证了以最大信噪比(SNR)为分离准则的盲分离算法对振动信号分离的可行性,并将该算法与阶次分析方法相结合,应用于汽车变速箱在降速实测振动信号的故障诊断中,实验结果表明以最大信噪比为准则的盲源分离算法具有计算准确度高及稳定性好的优点,取得了良好的效果。 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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基于独立分量分析的潜艇振源贡献量定量计算方法 总被引:4,自引:0,他引:4
振动噪声控制对于潜艇具有重要意义。常用的振动噪声分析方法仅分析了噪声的来源,而未对振动噪声源对总振动噪声的贡献量进行定量计算。介绍盲源分离基本模型,以及基于独立分量分析理论和聚类评价方法提高盲分离性能的改进固定点算法,并基于该算法和先验信息理论提出一种定量计算振源贡献量的新方法。通过仿真试验分析基于两种不同分离准则算法的分离性能以及振源贡献量计算结果。将该方法应用于某型号潜艇缩比模型振源贡献量的定量计算中,对比分析表明该方法具有很高的计算精度。研究结论可为振动噪声的主动控制提供可靠的依据。 相似文献
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由于旋转机械在运行过程中,传感器测得的振动信号是各振源的混叠信号且含有很强的噪声,常规的信号处理方法难以分离混叠信号,对设备的状态监测和故障诊断造成了很大的困难。针对这一问题,介绍了盲源分离基本原理和方法,指出源分离算法在脉冲噪声环境下失效。针对强脉冲噪声环境下的混叠振动信号,首先,通过中值滤波降噪方法对振动信号进行降噪;然后,通过盲源分离算法对降噪后的信号分离;最后,利用该方法对实测混叠转子振动信号成功实现了降噪和故障信号分离。仿真结果验证了提出方法的有效性。 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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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
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将人工免疫算法用于盲源分离算法,阐述了盲源分离过程,提出了免疫优化盲源分离算法(AIS-ICA算法),针对4组特定信号的混合与分离进行了仿真试验。仿真试验结果表明,该算法具有收敛速度快、分离精度高和稳定性好等优点。将该算法用于齿轮箱振动信号的盲源分离及其故障诊断,增强了振动信号所携带的故障信息,结果表明该算法用于齿轮箱振动信号分离可增强故障信息,降低齿轮箱故障诊断难度。 相似文献