Description: 经验模态分析（EMD）的MATLAB计算算法，经验模态分析是机械振动分析领域的重要方法-Empirical mode decomposition (EMD) of the MATLAB computing method, empirical mode analysis is an important field of mechanical vibration analysis method Platform: |
Size: 6144 |
Author:Sun |
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Description: 对振动信号进行的EMD分解程序，学习振动的同学可以-Of the vibration signal of EMD decomposition process, students can learn a good look at the vibration Platform: |
Size: 8192 |
Author:xuyuanbo |
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Description: 小波包分析提取振动信号中的分解，系数重构特征频率，以及能量谱分析计算-Wavelet packet analysis to extract the vibration signal decomposition coefficient reconstruction of the characteristic frequency, and calculate the energy spectrum analysis Platform: |
Size: 14336 |
Author:ifengdoo |
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Description: 基于matlab的小波包分解应用于机械振动信号的故障分析-Used in failure analysis of mechanical vibration signals based on the Matlab wavelet packet decomposition Platform: |
Size: 1024 |
Author:文涛 |
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Description: 采用基于奇异值分解和人工神经网络的多传感器数据融合方法对喷水推进泵的空化状态进行了分类识别研究。首先利用基于奇异值分解的权值估计算法分别对水声信号和振动信号在时间上进行数据级融合，提取出各自的特征，然后将所有特征组合起来作为神经网络的输入，利用ＢＰ网络和ＲＢＦ网络进行特征级融合和分类识别。-The use of water jet propulsion pump cavitation state multi-sensor data fusion method based on singular value decomposition and artificial neural network classification and recognition. First, based on the singular value decomposition weights estimation algorithm level data fusion underwater acoustic signals and vibration signals in time, extract individual characteristics, then combined all features as the input of the neural network, using BP and RBF network feature fusion and classification. Platform: |
Size: 1116160 |
Author:张力 |
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Description: 提出了将信号进行相空间重构后再采用奇异值分解, 对分解后的主成分进行包络分析, 从而提取信号的隐含特
征的方法, 并将该方法应用于齿轮的局部故障振动特征信号的提取中。数值仿真实验结果表明, 该方法能有效提取强背景
信号及噪声中的弱冲击特征信号, 是一种有效的弱信号特征提取方法。采用该方法对齿轮振动信号进行故障特征提取与识
别, 结果与实际情况相符。-Signal implicit characteristic of phase space reconstruction, and then using the singular value decomposition (SVD), principal component decomposition envelopment analysis, so as to extract the signal, and the method is applied to the partial failure of the vibration characteristics of the signal of the gear extraction. The numerical simulation results show that this method can effectively extract Weak Feature strong background signal and noise in the signal, a weak signal feature extraction methods. The extraction and recognition of fault feature of gear vibration signal results consistent with the actual situation. Platform: |
Size: 278528 |
Author:张力 |
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Description: 测量6205深沟球轴承的故障振动加速度信号, 对信号进行时频分析, 利用经验模态分解方法将振动信号分解成不同特征时间尺度的固有模态函数,对每个固有模态函数进行Hilbert 变换得到Hilbert 谱,通过谱分析识别轴承的故障部位和类型, 证实Hilbert 谱的有效性-Measuring 6205 deep groove ball bearing fault vibration acceleration signal, the signal frequency analysis, empirical mode decomposition method to decompose the vibration signal into different characteristic time scales of intrinsic mode functions, the Hilbert transform of each intrinsic mode functions Hilbert spectrum, spectral analysis to identify the location and type of bearing failure, confirmed the effectiveness of the Hilbert spectrum Platform: |
Size: 694272 |
Author:张力 |
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Description: y=d2wavelet(x,Fs,level) does the 2nd order Daubechies Wavelet Transform of signal x with a sampling frequency Fs and the DWT is decomposition is done upto a level
It returns the matrix of all decompositions and the final approximations.
Instead of using the matlab s inbuilt DWT function, this file explains the algorithm for DWT. Mostly useful for learning & academic purposes.
For other wavelets, the filter values alone can be changed or WFILTERS can
be used.
The function basically is for Condition Monitoring of rotating equipments by vibration based bearing fault diagnosis by the author.
Example:
clear all
t=[0:0.0003:8*pi]
x=sin(5000*t)+sin(1000*t)
x=x(1:2^16)
level=5 Fs=1/0.003
d2wavelet(x,Fs,level)
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-y=d2wavelet(x,Fs,level) does the 2nd order Daubechies Wavelet Transform of signal x with a sampling frequency Fs and the DWT is decomposition is done upto a level
It returns the matrix of all decompositions and the final approximations.
Instead of using the matlab s inbuilt DWT function, this file explains the algorithm for DWT. Mostly useful for learning & academic purposes.
For other wavelets, the filter values alone can be changed or WFILTERS can
be used.
The function basically is for Condition Monitoring of rotating equipments by vibration based bearing fault diagnosis by the author.
Example:
clear all
t=[0:0.0003:8*pi]
x=sin(5000*t)+sin(1000*t)
x=x(1:2^16)
level=5 Fs=1/0.003
d2wavelet(x,Fs,level)
Thanks for Downloading. Don t forget to rate or comment.
Platform: |
Size: 2048 |
Author:无界 |
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Description: 一个小波包分解重构函数，用于振动信号的故障诊断-A wavelet packet decomposition and reconstruction functions for fault diagnosis of the vibration signal Platform: |
Size: 1024 |
Author:李枝荣 |
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Description: The Empirical Mode Decomposition has become very populars in ceits first introduc
tion.Its suitability and expected performance for specific signal processing task is
however some what openended. Addressed are basic questions concerning the decomposition of ignals according to
different timescales,from noise sensitivityt of requency resolution.Comparing it to the
recently introduced Hilbert Vibration Decomposition, Platform: |
Size: 3385344 |
Author:唐建 |
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Description: The HHT represents a time-dependent series in
a two-dimensional (2-D) time-frequency domain by extracting
instan eous frequency components within the signal through
an Empirical Mode Decomposition (EMD) process. The analytical
background of the HHT is introduced, based on a synthetic
analytic signal, and its effectiveness is experimentally evaluated
using vibration signals measured on a test bearing. The results
demonstrate that HHT is suited for capturing transient events in
dynamic systems such as the propagation of structural defects in a
rolling bearing, thus providing a viable signal processing tool for
machine health monitoring.-The HHT represents a time-dependent series in
a two-dimensional (2-D) time-frequency domain by extracting
instantaneous frequency components within the signal through
an Empirical Mode Decomposition (EMD) process. The analytical
background of the HHT is introduced, based on a synthetic
analytic signal, and its effectiveness is experimentally evaluated
using vibration signals measured on a test bearing. The results
demonstrate that HHT is suited for capturing transient events in
dynamic systems such as the propagation of structural defects in a
rolling bearing, thus providing a viable signal processing tool for
machine health monitoring. Platform: |
Size: 1998848 |
Author:唐建 |
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Description: This paper describes a method for detecting faults
in the tap selector by means of vibration measurements during tap
changer operation, using envelope analysis based on Hilbert transform
and wavelet decomposition. Different failures at the tap selector
can be distinguished in the vibration signature as they are
reflected in different parts of that signature. The diagnosis parameters
allowing the most selective failure classification are found. Platform: |
Size: 2020352 |
Author:唐建 |
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Description: matlab在振动信号处理中的应用。通过matlab编程语言将复杂的振动信号通过算法进行消噪，信号分解与重构。-The application of matlab in vibration signal processing.Through the matlab programming language to deal with the complex vibration signal, by using algorithmde to de-noising, signal decomposition and reconstruction. Platform: |
Size: 1024 |
Author:杨珖 |
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Description: 针对信号处理方面，该例子实现了希尔伯特振动分解的，(For signal processing, this example implements the Hilbert vibration decomposition.) Platform: |
Size: 13312 |
Author:落叶无痕12 |
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Search - Vibration Decomposition