搜索资源列表
wavelet-in-signal-processing
- 小波变换在信号处理中的应用,包括分解，去噪，以及检测传感器故障等
EMD-Toolbox
- EMD的Toolbox及使用方法 经验模态分解（Empirical Mode Decomposition, 简称EMD）是由美国NASA的黄锷博士提出的一种信号分析方法.它依据数据自身的时间尺度特征来进行信号分解, 无须预先设定任何基函数。这一点与建立在先验性的谐波基函数和小波基函数上的傅里叶分解与小波分解方法具有本质性的差别。正是由于这样的特点, EMD 方法在理论上可以应用于任何类型的信号的分解, 因而在处理非平稳及非线性数据上, 具有非常明显的优势。所以, EMD方法一经提出就在不同的
wavelet
- matlab小波分析源程序，包含小波分解去噪，奇异点检测，实际工程中的故障检测，图像处理-matlab
WavletDecomposeTheFaultSignalPower
- 选用DB小波对故障电力信号进行分解，从图中可轻易判断出故障点。-DB wavelet used to decompose the fault signal power, from the figure can easily determine the point of failure.
xiaobosuanfa
- 采样频率 fs=10000 轴承外环故障信号 fid=fopen( bearingout.dat , r ) 故障 N=1024 xdata=fread(fid,N, int16 ) fclose(fid) xdata=(xdata-mean(xdata))/std(xdata,1) 时域波形 figure(1) plot(1:N,xdata) xlabel( 时间 t/n ) ylabel( 电压 V/v )
hhthht
- 希尔伯特-黄变换在电力故障中的应用 内含EMD分解 希尔伯特变换算法-Hilbert- Huang transform in power failure in the application
VMD
- 可以实现滚动轴承的故障采集处理，变分模态分解法很强大(Rolling bearing fault acquisition and processing can be realized, and the variational modal decomposition method is very powerful)
p_wavelet
- 小波分解轴承故障诊断代码，包括特征提取，用的数据是西储大学轴承数据(Wavelet decomposition bearing fault diagnosis code, including feature extraction, the data used is the West Park University bearing data)
基于小波包提取轴承故障
- 基于小波包变换对信号进行分解，提取机器轴承故障的特征信号进行损伤识别，(The signal is decomposed based on wavelet packet transform, and the characteristic signals of machine bearing faults are extracted to identify the damage,)
emd+信息熵
- 可以实现机械EMD经验模态分解，提取特征量并利用神经网络进行模式识别故障类型(The empirical mode decomposition of mechanical EMD can be realized, feature quantity is extracted and neural network is used to identify the type of fault.)
轴承故障
- 轴承故障诊断，用于分析轴承外圈故障的经验模态分解(Bearing fault diagnosis)
rParabEmd__L
- 该程序对信号进行经验模式分解，可以用于故障信号处理，还可以与很对方法结合。如，排列熵，emd,vmd,等(This program performs the Empirical Mode Decomposition accordingly to the signal)
局部经验模态分解
- 基于局部经验模态分解，可以很好地提取轴承故障特征
vmd
- 变分模态分解,用于分解各种信号，可用来故障诊断，特征提取。(Variational mode decomposition, used to decompose various signals, can be used for fault diagnosis and feature extraction.)
VMDtest
- 改进的变分模态分解算法，有效提取振动信号中故障频率。(The improved variational mode decomposition algorithm can effectively extract the fault frequency in vibration signals.)
小波信息熵
- 对信号进行小波变换，确定小波基的选择和分解层数，再求解信号的信息熵，可用于故障诊断。(The wavelet transform is performed on the signal to determine the selection and decomposition layers of the wavelet base, and then the information entropy of the signal is solved, which can be used for fault
用于信号的EMD、EEMD、VMD分解
- 用于信号的分解、降噪和重构，实现故障诊断(Used for signal decomposition, noise reduction and reconstruction to realize fault diagnosis)
变分模态方法
- 变分模态分解方法能够使一个多频带的故障信号，分解出具有单个频带的子信号，然后使用共振解调方法可实现故障信号的诊断。(VMD method can decompose a multi band fault signal into a single band sub signal, and then use resonance demodulation method to realize fault signal diagnosis.)
xiaobo
- 对故障数据的小波包分解与信号重构、小波包能量特征提取 暨 小波包分解后实现按频率大小分布重新排列，并进行降噪处理。(After wavelet packet decomposition and signal reconstruction, wavelet packet energy feature extraction and wavelet packet decomposition, the fault data can be rearranged according to the frequ
匹配追踪算法
- 共振稀疏分解常用算法匹配追踪法，用于轴承故障的分离(Resonance sparse decomposition algorithm matching tracking method is commonly used for bearing fault separation)